bspline.hpp

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#pragma once
 
#include <algorithm>
#include <exception>
#include <filesystem>
#include <functional>
#include <regex>
 
#include <core/core.hpp>
#include <core/options.hpp>
#include <splines/patch.hpp>
#include <utils/blocktensor.hpp>
#include <utils/container.hpp>
#include <utils/fqn.hpp>
#include <utils/index_sequence.hpp>
#include <utils/integer_pow.hpp>
#include <utils/linalg.hpp>
#include <utils/serialize.hpp>
#include <utils/tensorarray.hpp>
#include <utils/vslice.hpp>
 
#define GENERATE_EXPR_SEQ (curl)(div)(grad)(hess)(jac)(lapl)
 
#define GENERATE_IEXPR_SEQ (icurl)(idiv)(igrad)(ihess)(ijac)(ilapl)
 
namespace iganet {
  
using namespace literals;
using utils::operator+;
 
//  clang-format off
enum class init : short_t {
  none = 0,     
  zeros = 1,    
  ones = 2,     
  linear = 3,   
  random = 4,   
  greville = 5, 
  linspace = 6  
};
 
enum class deriv : short_t {
  func = 0,  
  dx = 1,    
  dy = 10,   
  dz = 100,  
  dt = 1000, 
};
//  clang-format on
  
inline constexpr auto operator+(deriv lhs, deriv rhs) {
  return static_cast<deriv>(static_cast<short_t>(lhs) +
                            static_cast<short_t>(rhs));
}
 
inline constexpr auto operator^(deriv lhs, short_t rhs) {
  return static_cast<deriv>(static_cast<short_t>(lhs) *
                            static_cast<short_t>(rhs));
}
 
namespace detail {
 
// @brief Concept to identify template parameters that have a
// find_knot_indices function  
template <typename T>
concept HasFindKnotIndices = requires(T t, typename T::eval_type x) {
  { t.find_knot_indices(x) };
};
 
// @brief Concept to identify template parameters that have a
// find_coeff_indices function    
template <typename T>
concept HasFindCoeffIndices = requires(T t, typename T::eval_type x) {
  { t.find_coeff_indices(x) };
};
  
} // namespace detail
  
class SplineCore_ {};
 
class UniformSplineCore_ : public SplineCore_ {};
 
class NonUniformSplineCore_ : public SplineCore_ {};
 
template <typename T>
concept SplineCoreType = std::is_base_of_v<SplineCore_, T>;
 
template <typename T>
concept UniformSplineCoreType = std::is_base_of_v<UniformSplineCore_, T>;
 
template <typename T>
concept NonUniformSplineCoreType = std::is_base_of_v<NonUniformSplineCore_, T>;
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
class UniformBSplineCore
    : public UniformSplineCore_,
      public utils::Serializable,
      public BSplinePatch<real_t, GeoDim, sizeof...(Degrees)> {
  template <typename BSplineCore>
    requires SplineCoreType<BSplineCore>
  friend class BSplineCommon;
 
protected:
  static constexpr const short_t parDim_ = sizeof...(Degrees);
 
  static constexpr const short_t geoDim_ = GeoDim;
 
  static constexpr const std::array<short_t, parDim_> degrees_ = {Degrees...};
 
  std::array<int64_t, parDim_> nknots_;
 
  std::array<int64_t, parDim_> ncoeffs_;
 
  std::array<int64_t, parDim_> ncoeffs_reverse_;
 
  utils::TensorArray<parDim_> knots_;
 
  utils::TensorArray<geoDim_> coeffs_;
 
  Options<real_t> options_;
 
public:
  using value_type = real_t;
 
  template <template <typename, short_t, short_t...> class BSpline,
            std::make_signed_t<short_t> degree_elevate = 0>
  using derived_type = BSpline<real_t, GeoDim, (Degrees + degree_elevate)...>;
 
  template <std::make_signed_t<short_t> degree_elevate = 0>
  using self_type = derived_type<UniformBSplineCore, degree_elevate>;
 
  template <typename other_t, short_t GeoDim_, short_t... Degrees_>
  using derived_self_type = UniformBSplineCore<other_t, GeoDim_, Degrees_...>;
 
  template <typename other_t>
  using real_derived_self_type =
      UniformBSplineCore<other_t, GeoDim, Degrees...>;
 
  [[nodiscard]] inline torch::Device device() const noexcept override {
    return options_.device();
  }
 
  [[nodiscard]] inline int32_t device_index() const noexcept override {
    return options_.device_index();
  }
 
  [[nodiscard]] inline torch::Dtype dtype() const noexcept override {
    return options_.dtype();
  }
 
  [[nodiscard]] inline torch::Layout layout() const noexcept override {
    return options_.layout();
  }
 
  [[nodiscard]] inline bool requires_grad() const noexcept override {
    return options_.requires_grad();
  }
 
  [[nodiscard]] inline bool pinned_memory() const noexcept override {
    return options_.pinned_memory();
  }
 
  [[nodiscard]] inline bool is_sparse() const noexcept override {
    return options_.is_sparse();
  }
 
  inline static constexpr bool is_uniform() noexcept { return true; }
 
  inline static constexpr bool is_nonuniform() noexcept { return false; }
 
  inline UniformBSplineCore &
  set_requires_grad(bool requires_grad) noexcept override {
    if (options_.requires_grad() == requires_grad)
      return *this;
 
    for (short_t i = 0; i < parDim_; ++i)
      knots_[i].set_requires_grad(requires_grad);
 
    for (short_t i = 0; i < geoDim_; ++i)
      coeffs_[i].set_requires_grad(requires_grad);
 
    Options<real_t> tmp(options_.requires_grad(requires_grad));
    options_.~Options<real_t>();
    new (&options_) Options<real_t>(tmp);
 
    return *this;
  }
 
  inline const Options<real_t> &options() const noexcept { return options_; }
 
  explicit UniformBSplineCore(Options<real_t> options = Options<real_t>{})
      : options_(options) {
    nknots_.fill(0);
    ncoeffs_.fill(0);
    ncoeffs_reverse_.fill(0);
  }
 
  explicit UniformBSplineCore(const std::array<int64_t, parDim_> &ncoeffs,
                              enum init init = init::greville,
                              Options<real_t> options = Options<real_t>{})
      : ncoeffs_(ncoeffs), ncoeffs_reverse_(ncoeffs), options_(options) {
    // Reverse ncoeffs
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    // Initialize knot vectors
    init_knots();
 
    // Initialize coefficients
    init_coeffs(init);
  }
 
  UniformBSplineCore(const std::array<int64_t, parDim_> &ncoeffs,
                     const utils::TensorArray<geoDim_> &coeffs,
                     bool clone = false,
                     Options<real_t> options = Options<real_t>{})
      : ncoeffs_(ncoeffs), ncoeffs_reverse_(ncoeffs), options_(options) {
    // Reverse ncoeffs
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    // Initialize knot vectors
    init_knots();
 
    // Check compatibility
    for (short_t i = 0; i < geoDim_; ++i)
      if (coeffs[i].numel() != ncumcoeffs())
        throw std::runtime_error("Invalid number of coefficients");
 
    // Copy/clone coefficients
    if (clone)
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = coeffs[i]
                         .clone()
                         .to(options.requires_grad(false))
                         .requires_grad_(options.requires_grad());
    else
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = coeffs[i];
  }
 
  UniformBSplineCore(const std::array<int64_t, parDim_> &ncoeffs,
                     utils::TensorArray<geoDim_> &&coeffs,
                     Options<real_t> options = Options<real_t>{})
      : ncoeffs_(ncoeffs), ncoeffs_reverse_(ncoeffs),
        coeffs_(std::move(coeffs)), options_(options) {
    // Reverse ncoeffs
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    // Initialize knot vectors
    init_knots();
  }
 
  template <typename other_t>
  explicit UniformBSplineCore(
      const UniformBSplineCore<other_t, GeoDim, Degrees...> &other,
      Options<real_t> options = Options<real_t>{})
      : nknots_(other.nknots()), ncoeffs_(other.ncoeffs()),
        ncoeffs_reverse_(ncoeffs_), options_(options) {
    // Reverse ncoeffs
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    // Clone coefficients
    for (short_t i = 0; i < geoDim_; ++i)
      coeffs_[i] = other.coeffs(i)
                       .clone()
                       .to(options.requires_grad(false))
                       .requires_grad_(options.requires_grad());
 
    // Clone knot vectors
    for (short_t i = 0; i < parDim_; ++i)
      knots_[i] = other.knots(i)
                      .clone()
                      .to(options.requires_grad(false))
                      .requires_grad_(options.requires_grad());
  }
 
  ~UniformBSplineCore() override = default;
 
  inline static constexpr short_t parDim() noexcept { return parDim_; }
 
  inline static constexpr short_t geoDim() noexcept { return geoDim_; }
 
  inline static constexpr const std::array<short_t, parDim_> &
  degrees() noexcept {
    return degrees_;
  }
 
  inline static constexpr short_t degree(short_t i) noexcept {
    assert(i >= 0 && i < parDim_);
    return degrees_[i];
  }
 
  inline const utils::TensorArray<parDim_> &knots() const noexcept {
    return knots_;
  }
 
  [[nodiscard]] inline const torch::Tensor &knots(short_t i) const noexcept {
    assert(i >= 0 && i < parDim_);
    return knots_[i];
  }
 
  inline utils::TensorArray<parDim_> &knots() noexcept { return knots_; }
 
  inline torch::Tensor &knots(short_t i) noexcept {
    assert(i >= 0 && i < parDim_);
    return knots_[i];
  }
 
  inline const std::array<int64_t, parDim_> &nknots() const noexcept {
    return nknots_;
  }
 
  [[nodiscard]] inline int64_t nknots(short_t i) const noexcept {
    assert(i >= 0 && i < parDim_);
    return nknots_[i];
  }
 
  inline const utils::TensorArray<geoDim_> &coeffs() const noexcept {
    return coeffs_;
  }
 
  [[nodiscard]] inline const torch::Tensor &coeffs(short_t i) const noexcept {
    assert(i >= 0 && i < geoDim_);
    return coeffs_[i];
  }
 
  inline utils::TensorArray<geoDim_> &coeffs() noexcept { return coeffs_; }
 
  inline torch::Tensor &coeffs(short_t i) noexcept {
    assert(i >= 0 && i < geoDim_);
    return coeffs_[i];
  }
 
  inline utils::TensorArray<geoDim_> coeffs_view() const noexcept {
    utils::TensorArray<geoDim_> coeffs;
    for (short_t i = 0; i < geoDim_; ++i)
      coeffs[i] = coeffs_view(i);
    return coeffs;
  }
 
  inline const auto coeffs_view(short_t i) const noexcept {
    assert(i >= 0 && i < geoDim_);
    if constexpr (parDim_ > 1)
      if (coeffs_[i].dim() > 1)
        return coeffs_[i].view(utils::to_ArrayRef(ncoeffs_reverse_) + (-1_i64));
      else
        return coeffs_[i].view(utils::to_ArrayRef(ncoeffs_reverse_));
    else
      return coeffs_[i];
  }
 
  [[nodiscard]] inline int64_t ncumcoeffs() const noexcept {
    int64_t s = 1;
 
    for (short_t i = 0; i < parDim_; ++i)
      s *= ncoeffs(i);
 
    return s;
  }
 
  inline const std::array<int64_t, parDim_> &ncoeffs() const noexcept {
    return ncoeffs_;
  }
 
  [[nodiscard]] inline int64_t ncoeffs(short_t i) const noexcept {
    assert(i >= 0 && i < parDim_);
    return ncoeffs_[i];
  }
 
private:
  template <std::size_t... Is>
  inline torch::Tensor as_tensor_(std::index_sequence<Is...>) const noexcept {
    return torch::cat({coeffs_[Is]...});
  }
 
public:
  [[nodiscard]] inline torch::Tensor as_tensor() const noexcept override {
    return as_tensor_(std::make_index_sequence<geoDim_>{});
  }
 
private:
  template <std::size_t... Is>
  inline UniformBSplineCore &
  from_tensor_(std::index_sequence<Is...>,
               const torch::Tensor &tensor) noexcept {
    ((coeffs_[Is] = tensor.index(
          {torch::indexing::Slice(Is * ncumcoeffs(), (Is + 1) * ncumcoeffs()),
           "..."})),
     ...);
    return *this;
  }
 
public:
  inline UniformBSplineCore &
  from_tensor(const torch::Tensor &tensor) noexcept override {
    return from_tensor_(std::make_index_sequence<geoDim_>{}, tensor);
  }
 
  //
  [[nodiscard]] inline int64_t as_tensor_size() const noexcept override {
    return geoDim_ * ncumcoeffs();
  }
 
  inline auto greville(bool interior = false) const {
    if constexpr (parDim_ == 0) {
      return torch::zeros(1, options_);
    } else {
      utils::TensorArray<parDim_> coeffs;
 
      // Fill coefficients with the tensor-product of Greville
      // abscissae values per univariate dimension
      for (short_t i = 0; i < parDim_; ++i) {
        coeffs[i] = torch::ones(1, options_);
 
        for (short_t j = 0; j < parDim_; ++j) {
          if (i == j) {
 
            int64_t offset = interior ? 1 : 0;
            int64_t count = ncoeffs_[j] - (interior ? 2 : 0);
 
            // idx_base: (count, 1)
            auto idx_base =
                torch::arange(
                    count,
                    options_.requires_grad(false).template dtype<int64_t>())
                    .unsqueeze(1);
 
            // offsets: (1, degree)
            auto offsets =
                torch::arange(
                    1, degrees_[j] + 1,
                    options_.requires_grad(false).template dtype<int64_t>())
                    .unsqueeze(0);
 
            // indices: (count, degree)
            auto indices = idx_base + offset + offsets;
 
            // Gather relevant knot values: shape (count, degree)
            auto gathered = knots_[j]
                                .index_select(0, indices.flatten())
                                .view({count, degrees_[j]});
 
            // Compute mean along degree dimension (dim=1)
            auto greville_ = gathered.mean(1);
 
            coeffs[i] = torch::kron(greville_, coeffs[i]);
          } else
            coeffs[i] = torch::kron(
                torch::ones(ncoeffs_[j] - (interior ? 2 : 0), options_),
                coeffs[i]);
        }
 
        // Enable gradient calculation for non-leaf tensor
        if (options_.requires_grad())
          coeffs[i].retain_grad();
      }
 
      return coeffs;
    }
  }
 
  inline utils::BlockTensor<torch::Tensor, 1, geoDim_>
  eval_from_precomputed(const torch::Tensor &basfunc,
                        const torch::Tensor &coeff_indices, int64_t numeval,
                        torch::IntArrayRef sizes) const override {
 
    utils::BlockTensor<torch::Tensor, 1, geoDim_> result;
 
    for (short_t i = 0; i < geoDim_; ++i)
      result.set(
          i, utils::dotproduct(
                 basfunc,
                 coeffs(i).index_select(0, coeff_indices).view({-1, numeval}))
                 .view(sizes));
    return result;
  }
 
  inline utils::BlockTensor<torch::Tensor, 1, geoDim_>
  eval_from_precomputed(const utils::TensorArray<parDim_> &basfunc,
                        const torch::Tensor &coeff_indices, int64_t numeval,
                        torch::IntArrayRef sizes) const override {
 
    utils::BlockTensor<torch::Tensor, 1, geoDim_> result;
 
    if constexpr (parDim_ == 0) {
      for (short_t i = 0; i < geoDim_; ++i)
        result.set(i, coeffs_[i]);
    }
 
    else {
      // Lambda expression to evaluate the spline function
      std::function<torch::Tensor(short_t, short_t)> eval_;
 
      eval_ = [&, this](short_t i, short_t dim) {
        if (dim == 0) {
          return torch::matmul(coeffs(i)
                                   .index_select(0, coeff_indices)
                                   .view({numeval, -1, degrees_[0] + 1}),
                               basfunc[0].view({numeval, -1, 1}));
        } else {
          return torch::matmul(
              (eval_(i, dim - 1)).view({numeval, -1, degrees_[dim] + 1}),
              basfunc[dim].view({numeval, -1, 1}));
        }
      };
 
      for (short_t i = 0; i < geoDim_; ++i)
        result.set(i, (eval_(i, parDim_ - 1)).view(sizes));
    }
    return result;
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const torch::Tensor &xi) const {
    if constexpr (parDim_ == 1)
      return eval<deriv, memory_optimized>(utils::TensorArray1({xi}));
    else
      throw std::runtime_error("Invalid parametric dimension");
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const utils::TensorArray<parDim_> &xi) const {
    return eval<deriv, memory_optimized>(xi, find_knot_indices(xi));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_tr(const torch::Tensor &xi) const {
    if constexpr (parDim_ == 1)
      return eval_tr<deriv, memory_optimized>(utils::TensorArray1({xi}));
    else
      throw std::runtime_error("Invalid parametric dimension");
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_tr(const utils::TensorArray<parDim_> &xi) const {
    return eval_tr<deriv, memory_optimized>(xi, find_knot_indices(xi));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const utils::TensorArray<parDim_> &xi,
                   const utils::TensorArray<parDim_> &knot_indices) const {
    return eval<deriv, memory_optimized>(
        xi, knot_indices, find_coeff_indices<memory_optimized>(knot_indices));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_tr(const utils::TensorArray<parDim_> &xi,
                   const utils::TensorArray<parDim_> &knot_indices) const {
    return eval_tr<deriv, memory_optimized>(
        xi, knot_indices, find_coeff_indices<memory_optimized>(knot_indices));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const utils::TensorArray<parDim_> &xi,
                   const utils::TensorArray<parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    utils::BlockTensor<torch::Tensor, 1, geoDim_> result;
 
    if constexpr (parDim_ == 0) {
      for (short_t i = 0; i < geoDim_; ++i)
        if constexpr (deriv == deriv::func)
          result.set(i, coeffs_[i]);
        else
          result.set(i, torch::zeros_like(coeffs_[i]));
      return result;
    } // parDim == 0
 
    else {
 
      // Check compatibility of arguments
      for (short_t i = 0; i < parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      if constexpr (memory_optimized) {
        // memory-optimized
 
        if (coeffs(0).dim() > 1)
          throw std::runtime_error(
              "Memory-optimized evaluation requires single-valued coefficient");
 
        else {
          auto basfunc =
              eval_basfunc<deriv, memory_optimized>(xi, knot_indices);
 
          // Lambda expression to evaluate the spline function
          std::function<torch::Tensor(short_t, short_t)> eval_;
 
          eval_ = [&, this](short_t i, short_t dim) {
            if (dim == 0) {
              return torch::matmul(
                  coeffs(i)
                      .index_select(0, coeff_indices)
                      .view({xi[0].numel(), -1, degrees_[0] + 1}),
                  basfunc[0].view({xi[0].numel(), -1, 1}));
            } else {
              return torch::matmul(
                  (eval_(i, dim - 1))
                      .view({xi[0].numel(), -1, degrees_[dim] + 1}),
                  basfunc[dim].view({xi[0].numel(), -1, 1}));
            }
          };
 
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, (eval_(i, parDim_ - 1)).view(xi[0].sizes()));
 
          return result;
        } // coeffs(0).dim() > 1
      }
 
      else {
        // not memory-optimized
 
        auto basfunc = eval_basfunc<deriv, memory_optimized>(xi, knot_indices);
        
        if (coeffs(0).dim() > 1) {
          // coeffs has extra dimension
          auto sizes = xi[0].sizes() + (-1_i64);
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, utils::dotproduct(basfunc.unsqueeze(-1),
                                            coeffs(i)
                                                .index_select(0, coeff_indices)
                                                .view({-1, xi[0].numel(),
                                                       coeffs(i).size(-1)}))
                              .view(sizes));
        } else {
          // coeffs does not have extra dimension
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, utils::dotproduct(basfunc,
                                            coeffs(i)
                                                .index_select(0, coeff_indices)
                                                .view({-1, xi[0].numel()}))
                              .view(xi[0].sizes()));
        }
        return result;
      }
    }
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_tr(const utils::TensorArray<parDim_> &xi,
                   const utils::TensorArray<parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    utils::BlockTensor<torch::Tensor, 1, geoDim_> result;
 
    if constexpr (parDim_ == 0) {
      for (short_t i = 0; i < geoDim_; ++i)
        if constexpr (deriv == deriv::func)
          result.set(i, coeffs_[i]);
        else
          result.set(i, torch::zeros_like(coeffs_[i]));
      return result;
    } // parDim == 0
 
    else {
 
      // Check compatibility of arguments
      for (short_t i = 0; i < parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      if constexpr (memory_optimized) {
        // memory-optimized
 
        if (coeffs(0).dim() > 1)
          throw std::runtime_error(
              "Memory-optimized evaluation requires single-valued coefficient");
 
        else {
          auto basfunc =
              eval_basfunc_tr<deriv, memory_optimized>(xi, knot_indices);
 
          // Lambda expression to evaluate the spline function
          std::function<torch::Tensor(short_t, short_t)> eval_;
 
          eval_ = [&, this](short_t i, short_t dim) {
            if (dim == 0) {
              return torch::matmul(
                  coeffs(i)
                      .index_select(0, coeff_indices)
                      .view({xi[0].numel(), -1, degrees_[0] + 1}),
                  basfunc[0].view({xi[0].numel(), -1, 1}));
            } else {
              return torch::matmul(
                  (eval_(i, dim - 1))
                      .view({xi[0].numel(), -1, degrees_[dim] + 1}),
                  basfunc[dim].view({xi[0].numel(), -1, 1}));
            }
          };
 
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, (eval_(i, parDim_ - 1)).view(xi[0].sizes()));
 
          return result;
        } // coeffs(0).dim() > 1
      }
 
      else {
        // not memory-optimized
 
        auto basfunc = eval_basfunc_tr<deriv, memory_optimized>(xi, knot_indices);
        
        if (coeffs(0).dim() > 1) {
          // coeffs has extra dimension
          auto sizes = xi[0].sizes() + (-1_i64);
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, utils::dotproduct(basfunc.unsqueeze(-1),
                                            coeffs(i)
                                                .index_select(0, coeff_indices)
                                                .view({-1, xi[0].numel(),
                                                       coeffs(i).size(-1)}))
                              .view(sizes));
        } else {
          // coeffs does not have extra dimension
          for (short_t i = 0; i < geoDim_; ++i)
            result.set(i, utils::dotproduct(basfunc,
                                            coeffs(i)
                                                .index_select(0, coeff_indices)
                                                .view({-1, xi[0].numel()}))
                              .view(xi[0].sizes()));
        }
        return result;
      }
    }
  }
 
  inline auto find_knot_indices(const torch::Tensor &xi) const noexcept {
    if constexpr (parDim_ == 0)
      return torch::zeros_like(coeffs_[0]).to(torch::kInt64);
    else
      return find_knot_indices(utils::TensorArray1({xi}));
  }
 
  inline utils::TensorArray<parDim_>
  find_knot_indices(const utils::TensorArray<parDim_> &xi) const noexcept {
    if constexpr (parDim_ == 0)
      return utils::TensorArray<parDim_>{};
    else {
      utils::TensorArray<parDim_> result;
 
      for (short_t i = 0; i < parDim_; ++i)
        result[i] =
            torch::min(
                torch::full_like(xi[i], ncoeffs_[i] - 1, options_),
                torch::floor(xi[i] * (ncoeffs_[i] - degrees_[i]) + degrees_[i]))
                .to(torch::kInt64);
 
      return result;
    }
  }
 
  template <bool memory_optimized = false>
  inline auto find_coeff_indices(const torch::Tensor &indices) const {
    if constexpr (parDim_ == 0)
      return torch::zeros_like(coeffs_[0]).to(torch::kInt64);
    else
      return find_coeff_indices<memory_optimized>(
          utils::TensorArray1({indices}));
  }
 
  template <bool memory_optimized = false>
  inline auto
  find_coeff_indices(const utils::TensorArray<parDim_> &indices) const {
    using utils::operator-;
 
    if constexpr (parDim_ == 0)
      return torch::zeros_like(coeffs_[0]).to(torch::kInt64);
    else if constexpr (parDim_ == 1)
      return utils::VSlice<memory_optimized>(indices[0].flatten(), -degrees_[0],
                                             1);
    else {
      return utils::VSlice<memory_optimized>(
          TENSORARRAY_FORALL(indices, flatten),
          utils::make_array<int64_t>(-degrees_),
          utils::make_array<int64_t, parDim_>(1),
          utils::remove_from_back(ncoeffs_));
    }
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc(const torch::Tensor &xi) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc<deriv, memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc(const utils::TensorArray<parDim_> &xi) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc<deriv, memory_optimized>(xi, find_knot_indices(xi));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc_tr(const torch::Tensor &xi) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc_tr<deriv, memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc_tr(const utils::TensorArray<parDim_> &xi) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc_tr<deriv, memory_optimized>(xi, find_knot_indices(xi));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc(const torch::Tensor &xi,
                           const torch::Tensor &knot_indices) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc<deriv, memory_optimized>(
          utils::TensorArray1({xi}), utils::TensorArray1({knot_indices}));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval_basfunc_tr(const torch::Tensor &xi,
                           const torch::Tensor &knot_indices) const {
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    } else
      return eval_basfunc_tr<deriv, memory_optimized>(
          utils::TensorArray1({xi}), utils::TensorArray1({knot_indices}));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto
  eval_basfunc(const utils::TensorArray<parDim_> &xi,
               const utils::TensorArray<parDim_> &knot_indices) const {
 
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    }
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      if constexpr (memory_optimized) {
 
        // Lambda expression to evaluate the vector of basis functions
        auto basfunc_ = [&,
                         this]<std::size_t... Is>(std::index_sequence<Is...>) {
          return utils::TensorArray<parDim_>{
              (eval_prefactor<degrees_[Is],
                              static_cast<short_t>(deriv) /
                                  utils::integer_pow<10, Is>::value % 10>() *
               eval_basfunc_univariate<degrees_[Is], Is,
                                       static_cast<short_t>(deriv) /
                                           utils::integer_pow<10, Is>::value %
                                           10>(xi[Is].flatten(),
                                               knot_indices[Is].flatten())
                   .transpose(0, 1))...};
        };
 
        return basfunc_(std::make_index_sequence<parDim_>{});
 
      }
 
      else /* not memory optimize */ {
 
        if constexpr (parDim_ == 1) {
          return eval_prefactor<degrees_[0],
                                static_cast<short_t>(deriv) % 10>() *
                 eval_basfunc_univariate<degrees_[0], 0,
                                         static_cast<short_t>(deriv) % 10>(
                     xi[0].flatten(), knot_indices[0].flatten());
 
        } else {
 
          // Lambda expression to evaluate the cumulated basis function
          auto basfunc_ = [&, this]<std::size_t... Is>(
                              std::index_sequence<Is...>) {
            return (1 * ... *
                    (eval_prefactor<degrees_[Is],
                                    static_cast<short_t>(deriv) /
                                        utils::integer_pow<10, Is>::value %
                                        10>())) *
            utils::kronproduct(
                       eval_basfunc_univariate<
                           degrees_[Is], Is,
                           static_cast<short_t>(deriv) /
                               utils::integer_pow<10, Is>::value % 10>(
                           xi[Is].flatten(), knot_indices[Is].flatten())...);
          };
 
          // Note that the kronecker product must be called in reverse order
          return basfunc_(utils::make_reverse_index_sequence<parDim_>{});
        }
      }
    }
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto
  eval_basfunc_tr(const utils::TensorArray<parDim_> &xi,
               const utils::TensorArray<parDim_> &knot_indices) const {
 
    if constexpr (parDim_ == 0) {
      if constexpr (deriv == deriv::func)
        return torch::ones_like(coeffs_[0]);
      else
        return torch::zeros_like(coeffs_[0]);
    }
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      if constexpr (memory_optimized) {
 
        // Lambda expression to evaluate the vector of basis functions
        auto basfunc_ = [&,
                         this]<std::size_t... Is>(std::index_sequence<Is...>) {
          return utils::TensorArray<parDim_>{
              (eval_prefactor<degrees_[Is],
                              static_cast<short_t>(deriv) /
                                  utils::integer_pow<10, Is>::value % 10>() *
               eval_basfunc_univariate_tr<degrees_[Is], Is,
                                       static_cast<short_t>(deriv) /
                                           utils::integer_pow<10, Is>::value %
                                           10>(xi[Is].flatten(),
                                               knot_indices[Is].flatten())
                   .transpose(0, 1))...};
        };
 
        return basfunc_(std::make_index_sequence<parDim_>{});
 
      }
 
      else /* not memory optimize */ {
 
        if constexpr (parDim_ == 1) {
          return eval_prefactor<degrees_[0],
                                static_cast<short_t>(deriv) % 10>() *
                 eval_basfunc_univariate_tr<degrees_[0], 0,
                                         static_cast<short_t>(deriv) % 10>(
                     xi[0].flatten(), knot_indices[0].flatten());
 
        } else {
 
          // Lambda expression to evaluate the cumulated basis function
          auto basfunc_ = [&, this]<std::size_t... Is>(
                              std::index_sequence<Is...>) {
            return (1 * ... *
                    (eval_prefactor<degrees_[Is],
                                    static_cast<short_t>(deriv) /
                                        utils::integer_pow<10, Is>::value %
                                        10>())) *
            utils::kronproduct<-1>(
                       eval_basfunc_univariate_tr<
                           degrees_[Is], Is,
                           static_cast<short_t>(deriv) /
                               utils::integer_pow<10, Is>::value % 10>(
                           xi[Is].flatten(), knot_indices[Is].flatten())...);
          };
 
          // Note that the kronecker product must be called in reverse order
          return basfunc_(utils::make_reverse_index_sequence<parDim_>{});
        }
      }
    }
  }
 
  inline UniformBSplineCore &
  transform(const std::function<
            std::array<real_t, geoDim_>(const std::array<real_t, parDim_> &)>
            mapping) {
 
    static_assert(parDim_ <= 4, "Unsupported parametric dimension");
 
    // 0D
    if constexpr (parDim_ == 0) {
      auto c = mapping(std::array<real_t, parDim_>{});
      for (short_t d = 0; d < geoDim_; ++d)
        coeffs_[d].detach()[0] = c[d];
    }
 
    // 1D
    else if constexpr (parDim_ == 1) {
#pragma omp parallel for
      for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
        auto c = mapping(
            std::array<real_t, parDim_>{i / real_t(ncoeffs_[0] - 1)});
        for (short_t d = 0; d < geoDim_; ++d)
          coeffs_[d].detach()[i] = c[d];
      }
    }
 
    // 2D
    else if constexpr (parDim_ == 2) {
#pragma omp parallel for collapse(2)
      for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
        for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
          auto c = mapping(std::array<real_t, parDim_>{
              i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1)});
          for (short_t d = 0; d < geoDim_; ++d)
            coeffs_[d].detach()[j * ncoeffs_[0] + i] = c[d];
        }
      }
    }
 
    // 3D
    else if constexpr (parDim_ == 3) {
#pragma omp parallel for collapse(3)
      for (int64_t k = 0; k < ncoeffs_[2]; ++k) {
        for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
          for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
            auto c = mapping(std::array<real_t, parDim_>{
                i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1),
                k / real_t(ncoeffs_[2] - 1)});
            for (short_t d = 0; d < geoDim_; ++d)
              coeffs_[d].detach()[k * ncoeffs_[0] * ncoeffs_[1] +
                                  j * ncoeffs_[0] + i] = c[d];
          }
        }
      }
    }
 
    // 4D
    else if constexpr (parDim_ == 4) {
#pragma omp parallel for collapse(4)
      for (int64_t l = 0; l < ncoeffs_[3]; ++l) {
        for (int64_t k = 0; k < ncoeffs_[2]; ++k) {
          for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
            for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
              auto c = mapping(std::array<real_t, parDim_>{
                  i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1),
                  k / real_t(ncoeffs_[2] - 1), l / real_t(ncoeffs_[3] - 1)});
              for (short_t d = 0; d < geoDim_; ++d)
                coeffs_[d]
                    .detach()[l * ncoeffs_[0] * ncoeffs_[1] * ncoeffs_[2] +
                              k * ncoeffs_[0] * ncoeffs_[1] + j * ncoeffs_[0] +
                              i] = c[d];
            }
          }
        }
      }
    } else
      throw std::runtime_error("Unsupported parametric dimension");
    
    return *this;
  }
 
  template <std::size_t N>
  inline UniformBSplineCore &
  transform(const std::function<
                std::array<real_t, N>(const std::array<real_t, parDim_> &)>
                mapping,
            std::array<short_t, N> dims) {
    
    static_assert(parDim_ <= 4, "Unsupported parametric dimension");
 
    // 0D
    if constexpr (parDim_ == 0) {
      auto c = mapping(std::array<real_t, parDim_>{});
      for (std::size_t d = 0; d < N; ++d)
        coeffs_[dims[d]].detach()[0] = c[d];
    }
 
    // 1D
    else if constexpr (parDim_ == 1) {
#pragma omp parallel for
      for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
        auto c = mapping(
            std::array<real_t, parDim_>{i / real_t(ncoeffs_[0] - 1)});
        for (std::size_t d = 0; d < N; ++d)
          coeffs_[dims[d]].detach()[i] = c[d];
      }
    }
 
    // 2D
    else if constexpr (parDim_ == 2) {
#pragma omp parallel for collapse(2)
      for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
        for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
          auto c = mapping(std::array<real_t, parDim_>{
              i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1)});
          for (std::size_t d = 0; d < N; ++d)
            coeffs_[dims[d]].detach()[j * ncoeffs_[0] + i] = c[d];
        }
      }
    }
 
    // 3D
    else if constexpr (parDim_ == 3) {
#pragma omp parallel for collapse(3)
      for (int64_t k = 0; k < ncoeffs_[2]; ++k) {
        for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
          for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
            auto c = mapping(std::array<real_t, parDim_>{
                i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1),
                k / real_t(ncoeffs_[2] - 1)});
            for (std::size_t d = 0; d < N; ++d)
              coeffs_[dims[d]].detach()[k * ncoeffs_[0] * ncoeffs_[1] +
                                        j * ncoeffs_[0] + i] = c[d];
          }
        }
      }
    }
 
    // 4D
    else if constexpr (parDim_ == 4) {
#pragma omp parallel for collapse(4)
      for (int64_t l = 0; l < ncoeffs_[3]; ++l) {
        for (int64_t k = 0; k < ncoeffs_[2]; ++k) {
          for (int64_t j = 0; j < ncoeffs_[1]; ++j) {
            for (int64_t i = 0; i < ncoeffs_[0]; ++i) {
              auto c = mapping(std::array<real_t, parDim_>{
                  i / real_t(ncoeffs_[0] - 1), j / real_t(ncoeffs_[1] - 1),
                  k / real_t(ncoeffs_[2] - 1), l / real_t(ncoeffs_[3] - 1)});
              for (std::size_t d = 0; d < N; ++d)
                coeffs_[dims[d]]
                    .detach()[l * ncoeffs_[0] * ncoeffs_[1] * ncoeffs_[2] +
                              k * ncoeffs_[0] * ncoeffs_[1] + j * ncoeffs_[0] +
                              i] = c[d];
            }
          }
        }
      }
    } else
      throw std::runtime_error("Unsupported parametric dimension");
    
    return *this;
  }
 
  [[nodiscard]] inline nlohmann::json to_json() const override {
    nlohmann::json json;
    json["degrees"] = degrees_;
    json["geoDim"] = geoDim_;
    json["parDim"] = parDim_;
    json["ncoeffs"] = ncoeffs_;
    json["nknots"] = nknots_;
    json["knots"] = knots_to_json();
    json["coeffs"] = coeffs_to_json();
 
    return json;
  }
 
  [[nodiscard]] inline nlohmann::json knots_to_json() const {
    return ::iganet::utils::to_json<real_t, 1>(knots_);
  }
 
  [[nodiscard]] inline nlohmann::json coeffs_to_json() const {
    auto coeffs_json = nlohmann::json::array();
    for (short_t g = 0; g < geoDim_; ++g) {
      auto [coeffs_cpu, coeffs_accessor] =
          utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
 
      auto json = nlohmann::json::array();
 
      if constexpr (parDim_ == 0) {
        json.push_back(coeffs_accessor[0]);
      }
 
      else {
        for (int64_t i = 0; i < ncumcoeffs(); ++i)
          json.push_back(coeffs_accessor[i]);
      }
 
      coeffs_json.push_back(json);
    }
    return coeffs_json;
  }
 
  inline UniformBSplineCore &from_json(const nlohmann::json &json) {
 
    if (json["geoDim"].get<short_t>() != geoDim_)
      throw std::runtime_error(
          "JSON object provides incompatible geometric dimensions");
 
    if (json["parDim"].get<short_t>() != parDim_)
      throw std::runtime_error(
          "JSON object provides incompatible parametric dimensions");
 
    if (json["degrees"].get<std::array<short_t, parDim_>>() != degrees_)
      throw std::runtime_error("JSON object provides incompatible degrees");
 
    nknots_ = json["nknots"].get<std::array<int64_t, parDim_>>();
    ncoeffs_ = json["ncoeffs"].get<std::array<int64_t, parDim_>>();
 
    // Reverse ncoeffs
    ncoeffs_reverse_ = ncoeffs_;
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    auto kv = json["knots"].get<std::array<std::vector<real_t>, parDim_>>();
 
    for (short_t i = 0; i < parDim_; ++i)
      knots_[i] = utils::to_tensor(kv[i], options_);
 
    auto c = json["coeffs"].get<std::array<std::vector<real_t>, geoDim_>>();
 
    for (short_t i = 0; i < geoDim_; ++i)
      coeffs_[i] = utils::to_tensor(c[i], options_);
 
    return *this;
  }
 
  [[nodiscard]] inline pugi::xml_document
  to_xml(int id = 0, const std::string &label = "", int index = -1) const {
    pugi::xml_document doc;
    pugi::xml_node root = doc.append_child("xml");
    to_xml(root, id, label, index);
 
    return doc;
  }
 
  inline pugi::xml_node &to_xml(pugi::xml_node &root, int id = 0,
                                const std::string &label = "",
                                int index = -1) const {
    // add Geometry node
    pugi::xml_node geo = root.append_child("Geometry");
 
    // 0D parametric dimension
    if constexpr (parDim_ == 0) {
      geo.append_attribute("type") = "Point";
 
      if (id >= 0)
        geo.append_attribute("id") = id;
 
      if (index >= 0)
        geo.append_attribute("index") = index;
 
      if (!label.empty())
        geo.append_attribute("label") = label.c_str();
    }
 
    // 1D parametric dimension
    else if constexpr (parDim_ == 1) {
      geo.append_attribute("type") = "BSpline";
 
      if (id >= 0)
        geo.append_attribute("id") = id;
 
      if (index >= 0)
        geo.append_attribute("index") = index;
 
      if (!label.empty())
        geo.append_attribute("label") = label.c_str();
 
      // add Basis node
      pugi::xml_node basis = geo.append_child("Basis");
      basis.append_attribute("type") = "BSplineBasis";
 
      // add KnotVector node
      pugi::xml_node knots = basis.append_child("KnotVector");
      knots.append_attribute("degree") = degrees_[0];
 
      std::stringstream ss;
      auto [knots_cpu, knots_accessor] =
          utils::to_tensorAccessor<real_t, 1>(knots_[0], torch::kCPU);
      for (int64_t i = 0; i < nknots_[0]; ++i)
        ss << std::to_string(knots_accessor[i])
           << (i < nknots_[0] - 1 ? " " : "");
      knots.append_child(pugi::node_pcdata).set_value(ss.str().c_str());
    }
 
    // >1D parametric dimension
    else {
      geo.append_attribute("type") =
          std::string("TensorBSpline").append(std::to_string(parDim_)).c_str();
 
      if (id >= 0)
        geo.append_attribute("id") = id;
 
      if (index >= 0)
        geo.append_attribute("index") = index;
 
      if (!label.empty())
        geo.append_attribute("label") = label.c_str();
 
      // add Basis node
      pugi::xml_node bases = geo.append_child("Basis");
      bases.append_attribute("type") = std::string("TensorBSplineBasis")
                                           .append(std::to_string(parDim_))
                                           .c_str();
 
      for (short_t index = 0; index < parDim_; ++index) {
        pugi::xml_node basis = bases.append_child("Basis");
        basis.append_attribute("type") = "BSplineBasis";
        basis.append_attribute("index") = index;
 
        // add KnotVector node
        pugi::xml_node knots = basis.append_child("KnotVector");
        knots.append_attribute("degree") = degrees_[index];
 
        std::stringstream ss;
        auto [knots_cpu, knots_accessor] =
            utils::to_tensorAccessor<real_t, 1>(knots_[index], torch::kCPU);
        for (int64_t i = 0; i < nknots_[index]; ++i)
          ss << std::to_string(knots_accessor[i])
             << (i < nknots_[index] - 1 ? " " : "");
        knots.append_child(pugi::node_pcdata).set_value(ss.str().c_str());
      }
 
    } // parametric dimension
 
    // add Coefs node
    pugi::xml_node coefs = geo.append_child("coefs");
    coefs.append_attribute("geoDim") = geoDim_;
 
    auto [coeffs_cpu, coeffs_accessors] =
        utils::to_tensorAccessor<real_t, 1>(coeffs_, torch::kCPU);
    std::stringstream ss;
 
    if constexpr (parDim_ == 0) {
      for (short_t g = 0; g < geoDim_; ++g)
        ss << std::to_string(coeffs_accessors[g][0]) << " ";
 
    } else {
      for (int64_t i = 0; i < utils::prod(ncoeffs_); ++i)
        for (short_t g = 0; g < geoDim_; ++g)
          ss << std::to_string(coeffs_accessors[g][i]) << " ";
    }
 
    coefs.append_child(pugi::node_pcdata).set_value(ss.str().c_str());
 
    return root;
  }
 
  inline UniformBSplineCore &from_xml(const pugi::xml_document &doc, int id = 0,
                                      const std::string &label = "",
                                      int index = -1) {
    return from_xml(doc.child("xml"), id, label, index);
  }
 
  inline UniformBSplineCore &from_xml(const pugi::xml_node &root, int id = 0,
                                      const std::string &label = "",
                                      int index = -1) {
 
    std::array<bool, std::max(parDim_, short_t{1})> nknots_found{false},
        ncoeffs_found{false};
 
    // Loop through all geometry nodes
    for (pugi::xml_node geo : root.children("Geometry")) {
 
      // 0D parametric dimension
      if constexpr (parDim_ == 0) {
 
        // Check for "Point" with given id, index, label
        if (geo.attribute("type").value() == std::string("Point") &&
            (id >= 0 ? geo.attribute("id").as_int() == id : true) &&
            (index >= 0 ? geo.attribute("index").as_int() == index : true) &&
            (!label.empty() ? geo.attribute("label").value() == label : true)) {
 
          nknots_found[0] = true;
          ncoeffs_found[0] = true;
        } // "Point"
        else
          continue; // try next "Geometry"
      }
 
      // 1D parametric dimension
      else if constexpr (parDim_ == 1) {
 
        // Check for "BSpline" with given id, index, label
        if (geo.attribute("type").value() == std::string("BSpline") &&
            (id >= 0 ? geo.attribute("id").as_int() == id : true) &&
            (index >= 0 ? geo.attribute("index").as_int() == index : true) &&
            (!label.empty() ? geo.attribute("label").value() == label : true)) {
 
          // Check for "BSplineBasis"
          if (pugi::xml_node basis = geo.child("Basis");
              basis.attribute("type").value() == std::string("BSplineBasis")) {
 
            // Check for "KnotVector"
            if (pugi::xml_node knots = basis.child("KnotVector");
                knots.attribute("degree").as_int() == degrees_[0]) {
 
              std::vector<real_t> kv;
              std::string values = std::regex_replace(
                  knots.text().get(), std::regex("[\t\r\n\a]+| +"), " ");
              for (auto value = strtok(&values[0], " "); value != nullptr;
                   value = strtok(nullptr, " "))
                kv.push_back(static_cast<real_t>(std::stod(value)));
 
              knots_[0] = utils::to_tensor(kv, options_);
              nknots_[0] = kv.size();
              ncoeffs_[0] = nknots_[0] - degrees_[0] - 1;
 
              nknots_found[0] = true;
              ncoeffs_found[0] = true;
 
            } // "KnotVector"
 
          } // "BSplineBasis"
 
        } // "Bspline"
        else
          continue; // try next "Geometry"
      }
 
      // >1D parametric dimension
      else {
 
        // Check for "TensorBSpline<parDim>" with given id, index, label
        if (geo.attribute("type").value() ==
                std::string("TensorBSpline").append(std::to_string(parDim_)) &&
            (id >= 0 ? geo.attribute("id").as_int() == id : true) &&
            (index >= 0 ? geo.attribute("index").as_int() == index : true) &&
            (!label.empty() ? geo.attribute("label").value() == label : true)) {
 
          // Check for "TensorBSplineBasis<parDim>"
          if (pugi::xml_node bases = geo.child("Basis");
              bases.attribute("type").value() ==
              std::string("TensorBSplineBasis")
                  .append(std::to_string(parDim_))) {
 
            // Loop through all basis nodes
            for (pugi::xml_node basis : bases.children("Basis")) {
 
              // Check for "BSplineBasis"
              if (basis.attribute("type").value() ==
                  std::string("BSplineBasis")) {
 
                int index = basis.attribute("index").as_int();
 
                // Check for "KnotVector"
                if (pugi::xml_node knots = basis.child("KnotVector");
                    knots.attribute("degree").as_int() == degrees_[index]) {
 
                  std::vector<real_t> kv;
                  std::string values = std::regex_replace(
                      knots.text().get(), std::regex("[\t\r\n\a]+| +"), " ");
 
                  for (auto value = strtok(&values[0], " "); value != nullptr;
                       value = strtok(nullptr, " "))
                    kv.push_back(static_cast<real_t>(std::stod(value)));
 
                  knots_[index] = utils::to_tensor(kv, options_);
                  nknots_[index] = kv.size();
                  ncoeffs_[index] = nknots_[index] - degrees_[index] - 1;
 
                  nknots_found[index] = true;
                  ncoeffs_found[index] = true;
 
                } // "KnotVector"
 
              } // "BSplineBasis"
 
            } // "Basis"
 
          } // "TensorBSplineBasis<parDim>"
 
        } // "TensorBSpline<parDim>"
        else
          continue; // try next "Geometry"
 
      } // parametric dimension
 
      if (std::any_of(std::begin(nknots_found), std::end(nknots_found),
                      [](bool i) { return !i; }))
        throw std::runtime_error(
            "XML object is not compatible with B-spline object");
 
      // Reverse ncoeffs
      ncoeffs_reverse_ = ncoeffs_;
      std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
      // Fill coefficients with zeros
      int64_t size = ncumcoeffs();
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = torch::zeros(size, options_.device(torch::kCPU));
 
      // Check for "coefs"
      if (pugi::xml_node coefs = geo.child("coefs")) {
 
        std::string values = std::regex_replace(
            coefs.text().get(), std::regex("[\t\r\n\a]+| +"), " ");
        auto coeffs_accessors = utils::to_tensorAccessor<real_t, 1>(coeffs_);
 
        if constexpr (parDim_ == 0) {
          auto value = strtok(&values[0], " ");
 
          for (short_t g = 0; g < geoDim_; ++g) {
            if (value == nullptr)
              throw std::runtime_error(
                  "XML object does not provide enough coefficients");
 
            coeffs_accessors[g][0] = static_cast<real_t>(std::stod(value));
            value = strtok(nullptr, " ");
          }
 
          if (value != nullptr)
            throw std::runtime_error(
                "XML object provides too many coefficients");
 
        } else {
          auto value = strtok(&values[0], " ");
 
          for (int64_t i = 0; i < utils::prod(ncoeffs_); ++i)
            for (short_t g = 0; g < geoDim_; ++g) {
              if (value == nullptr)
                throw std::runtime_error(
                    "XML object does not provide enough coefficients");
 
              coeffs_accessors[g][i] = static_cast<real_t>(std::stod(value));
              value = strtok(nullptr, " ");
            }
 
          if (value != nullptr)
            throw std::runtime_error(
                "XML object provides too many coefficients");
        }
 
        // Copy coefficients to device (if needed)
        for (short_t i = 0; i < geoDim_; ++i)
          coeffs_[i] = coeffs_[i].to(options_.device());
 
        if constexpr (parDim_ == 0) {
          if (nknots_found[0] && ncoeffs_found[0])
            return *this;
        } else if (std::all_of(std::begin(nknots_found), std::end(nknots_found),
                               [](bool i) { return i; }) &&
                   std::all_of(std::begin(ncoeffs_found),
                               std::end(ncoeffs_found),
                               [](bool i) { return i; }))
          return *this;
 
        else
          throw std::runtime_error(
              "XML object is not compatible with B-spline object");
 
      } // Coefs
      else
        throw std::runtime_error("XML object does not provide coefficients");
 
    } // "Geometry"
 
    throw std::runtime_error("XML object does not provide geometry with given "
                             "id, index, and/or label");
    return *this;
  }
 
  inline void load(const std::string &filename,
                   const std::string &key = "bspline") {
    torch::serialize::InputArchive archive;
    archive.load_from(filename);
    read(archive, key);
  }
 
  inline torch::serialize::InputArchive &
  read(torch::serialize::InputArchive &archive,
       const std::string &key = "bspline") {
    torch::Tensor tensor;
 
    archive.read(key + ".parDim", tensor);
    if (tensor.item<int64_t>() != parDim_)
      throw std::runtime_error("parDim mismatch");
 
    archive.read(key + ".geoDim", tensor);
    if (tensor.item<int64_t>() != geoDim_)
      throw std::runtime_error("geoDim mismatch");
 
    for (short_t i = 0; i < parDim_; ++i) {
      archive.read(key + ".degree[" + std::to_string(i) + "]", tensor);
      if (tensor.item<int64_t>() != degrees_[i])
        throw std::runtime_error("degrees mismatch");
    }
 
    for (short_t i = 0; i < parDim_; ++i) {
      archive.read(key + ".nknots[" + std::to_string(i) + "]", tensor);
      nknots_[i] = tensor.item<int64_t>();
    }
 
    for (short_t i = 0; i < parDim_; ++i)
      archive.read(key + ".knots[" + std::to_string(i) + "]", knots_[i]);
 
    for (short_t i = 0; i < parDim_; ++i) {
      archive.read(key + ".ncoeffs[" + std::to_string(i) + "]", tensor);
      ncoeffs_[i] = tensor.item<int64_t>();
    }
 
    ncoeffs_reverse_ = ncoeffs_;
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
    
    for (short_t i = 0; i < geoDim_; ++i)
      archive.read(key + ".coeffs[" + std::to_string(i) + "]", coeffs_[i]);
 
    return archive;
  }
 
  inline void save(const std::string &filename,
                   const std::string &key = "bspline") const {
    torch::serialize::OutputArchive archive;
    write(archive, key).save_to(filename);
  }
 
  inline torch::serialize::OutputArchive &
  write(torch::serialize::OutputArchive &archive,
        const std::string &key = "bspline") const {
    archive.write(key + ".parDim", torch::full({1}, parDim_));
    archive.write(key + ".geoDim", torch::full({1}, geoDim_));
 
    for (short_t i = 0; i < parDim_; ++i)
      archive.write(key + ".degree[" + std::to_string(i) + "]",
                    torch::full({1}, degrees_[i]));
 
    for (short_t i = 0; i < parDim_; ++i)
      archive.write(key + ".nknots[" + std::to_string(i) + "]",
                    torch::full({1}, nknots_[i]));
 
    for (short_t i = 0; i < parDim_; ++i)
      archive.write(key + ".knots[" + std::to_string(i) + "]", knots_[i]);
 
    for (short_t i = 0; i < parDim_; ++i)
      archive.write(key + ".ncoeffs[" + std::to_string(i) + "]",
                    torch::full({1}, ncoeffs_[i]));
 
    for (short_t i = 0; i < geoDim_; ++i)
      archive.write(key + ".coeffs[" + std::to_string(i) + "]", coeffs_[i]);
 
    return archive;
  }
 
  template <typename other_t, short_t GeoDim_, short_t... Degrees_>
  bool isclose(const UniformBSplineCore<other_t, GeoDim_, Degrees_...> &other,
               real_t rtol = real_t{1e-5}, real_t atol = real_t{1e-8}) const {
    if constexpr (!std::is_same_v<real_t, other_t>)
      return false;
    bool result(true);
 
    result *= (parDim_ == other.parDim());
    result *= (geoDim_ == other.geoDim());
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (degree(i) == other.degree(i));
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (nknots(i) == other.nknots(i));
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (ncoeffs(i) == other.ncoeffs(i));
 
    if (!result)
      return result;
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= torch::allclose(knots(i), other.knots(i), rtol, atol);
 
    for (short_t i = 0; i < geoDim_; ++i)
      result *= torch::allclose(coeffs(i), other.coeffs(i), rtol, atol);
 
    return result;
  }
 
  template <typename other_t, short_t GeoDim_, short_t... Degrees_>
  bool operator==(
      const UniformBSplineCore<other_t, GeoDim_, Degrees_...> &other) const {
    if constexpr (!std::is_same_v<real_t, other_t>)
      return false;
    bool result(true);
 
    result *= (parDim_ == other.parDim());
    result *= (geoDim_ == other.geoDim());
 
    if (!result)
      return result;
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (degree(i) == other.degree(i));
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (nknots(i) == other.nknots(i));
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= (ncoeffs(i) == other.ncoeffs(i));
 
    for (short_t i = 0; i < parDim_; ++i)
      result *= torch::equal(knots(i), other.knots(i));
 
    for (short_t i = 0; i < geoDim_; ++i)
      result *= torch::equal(coeffs(i), other.coeffs(i));
 
    return result;
  }
 
  template <typename other_t, short_t GeoDim_, short_t... Degrees_>
  bool operator!=(
      const UniformBSplineCore<other_t, GeoDim_, Degrees_...> &other) const {
    return !(
        *this ==
        other); // Do not change this to (*this != other) is it does not work
  }
 
  inline UniformBSplineCore &uniform_refine(int numRefine = 1, int dim = -1) {
    assert(numRefine > 0);
    assert(dim == -1 || (dim >= 0 && dim < parDim_));
 
    // Update number of knots and coefficients
    std::array<int64_t, parDim_> nknots(nknots_);
    std::array<int64_t, parDim_> ncoeffs(ncoeffs_);
 
    for (int refine = 0; refine < numRefine; ++refine) {
      if (dim == -1)
        for (short_t i = 0; i < parDim_; ++i) {
          ncoeffs[i] += nknots[i] - 2 * degrees_[i] - 1; // must be done first
          nknots[i] += nknots[i] - 2 * degrees_[i] - 1;
        }
      else {
        ncoeffs[dim] +=
            nknots[dim] - 2 * degrees_[dim] - 1; // must be done first
        nknots[dim] += nknots[dim] - 2 * degrees_[dim] - 1;
      }
    }
 
    // Update knot vectors
    utils::TensorArray<parDim_> knots, knots_indices;
 
    for (short_t i = 0; i < parDim_; ++i) {
      std::vector<real_t> kv;
      kv.reserve(nknots[i]);
 
      for (int64_t j = 0; j < degrees_[i]; ++j)
        kv.push_back(static_cast<real_t>(0));
 
      for (int64_t j = 0; j < ncoeffs[i] - degrees_[i] + 1; ++j)
        kv.push_back(static_cast<real_t>(j) /
                     static_cast<real_t>(ncoeffs[i] - degrees_[i]));
 
      for (int64_t j = 0; j < degrees_[i]; ++j)
        kv.push_back(static_cast<real_t>(1));
 
      knots[i] = utils::to_tensor(kv, options_);
    }
 
    // The updated knot vectors have lengths \f$m_d+p_d+1\f$, where
    // \f$m_d\f$ is the number of coefficients after the update. To
    // update the coefficients using the Oslo algorithm (Algorithm
    // 4.11 from \cite Lyche:2011) we need to neglect the last
    // \f$p_d+1\f$ knots in what follows
    for (short_t i = 0; i < parDim_; ++i)
      knots_indices[i] = knots[i].index(
          {torch::indexing::Slice(0, knots[i].numel() - degrees_[i] - 1)});
 
    // Get indices of the first \f$m_d\f$ new knots relative to old
    // knot vectors
    auto new_knot_indices = find_knot_indices(knots_indices);
 
    // Update coefficient vector
    update_coeffs(knots, new_knot_indices);
 
    // Swap old and new data
    knots.swap(knots_);
    nknots.swap(nknots_);
    ncoeffs.swap(ncoeffs_);
 
    ncoeffs_reverse_ = ncoeffs_;
    std::reverse(ncoeffs_reverse_.begin(), ncoeffs_reverse_.end());
 
    return *this;
  }
 
private:
  template <int64_t degree, int64_t deriv, int64_t terminal = degree - deriv>
  [[nodiscard]] inline int64_t constexpr eval_prefactor() const {
    if constexpr (degree > terminal)
      return degree * eval_prefactor<degree - 1, deriv, terminal>();
    else
      return 1;
  }
 
public:
  inline void init_knots() {
 
    for (short_t i = 0; i < parDim_; ++i) {
 
      // Check that open knot vector can be created
      if ((ncoeffs_[i] < degrees_[i] + 1) || (ncoeffs_[i] < 2))
        throw std::runtime_error(
            "Not enough coefficients to create open knot vector");
 
      nknots_[i] = ncoeffs_[i] + degrees_[i] + 1;
      int64_t num_inner = ncoeffs_[i] - degrees_[i];
 
      auto start = torch::zeros({degrees_[i]}, options_);
      auto end = torch::ones({degrees_[i]}, options_);
      auto inner = torch::empty({0}, options_);
 
      if (num_inner > 0) {
        inner = torch::arange(0, num_inner + 1, options_);
        inner = inner / static_cast<real_t>(num_inner);
      }
 
      knots_[i] = torch::cat({start, inner, end});
    }
  }
 
  inline void init_coeffs(enum init init) {
    switch (init) {
 
    case (init::none): {
      break;
    }
 
    case (init::zeros): {
 
      // Fill coefficients with zeros
      int64_t size = ncumcoeffs();
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = torch::zeros(size, options_);
 
      break;
    }
 
    case (init::ones): {
 
      // Fill coefficients with ones
      int64_t size = ncumcoeffs();
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = torch::ones(size, options_);
 
      break;
    }
 
    case (init::random): {
 
      // Fill coefficients with random values
      int64_t size = ncumcoeffs();
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = torch::rand(size, options_);
 
      break;
    }
 
    case (init::linear): {
 
      // Fill coefficients with the tensor-product of linearly
      // increasing values between 0 and 1 per univariate dimension
      for (short_t i = 0; i < geoDim_; ++i) {
        coeffs_[i] = torch::ones(1, options_);
 
        for (short_t j = 0; j < parDim_; ++j) {
          if (i == j)
            coeffs_[i] = torch::kron(torch::linspace(static_cast<real_t>(0),
                                                     static_cast<real_t>(1),
                                                     ncoeffs_[j], options_),
                                     coeffs_[i]);
          else
            coeffs_[i] =
                torch::kron(torch::ones(ncoeffs_[j], options_), coeffs_[i]);
        }
 
        // Enable gradient calculation for non-leaf tensor
        if (options_.requires_grad())
          coeffs_[i].retain_grad();
      }
      break;
    }
 
    case (init::greville): {
 
      // Fill coefficients with the tensor-product of Greville
      // abscissae values per univariate dimension
      for (short_t i = 0; i < geoDim_; ++i) {
        coeffs_[i] = torch::ones(1, options_);
 
        for (short_t j = 0; j < parDim_; ++j) {
          if (i == j) {
 
            int64_t count = ncoeffs_[j];
 
            // idx_base: (count, 1)
            auto idx_base =
                torch::arange(
                    count,
                    options_.requires_grad(false).template dtype<int64_t>())
                    .unsqueeze(1);
 
            // offsets: (1, degree)
            auto offsets =
                torch::arange(
                    1, degrees_[j] + 1,
                    options_.requires_grad(false).template dtype<int64_t>())
                    .unsqueeze(0);
 
            // indices: (count, degree)
            auto indices = idx_base + offsets;
 
            // Gather relevant knot values: shape (count, degree)
            auto gathered = knots_[j]
                                .index_select(0, indices.flatten())
                                .view({count, degrees_[j]});
 
            // Compute mean along degree dimension (dim=1)
            auto greville_ = gathered.mean(1);
 
            coeffs_[i] = torch::kron(greville_, coeffs_[i]);
          } else
            coeffs_[i] =
                torch::kron(torch::ones(ncoeffs_[j], options_), coeffs_[i]);
        }
 
        // Enable gradient calculation for non-leaf tensor
        if (options_.requires_grad())
          coeffs_[i].retain_grad();
      }
      break;
    }
 
    case (init::linspace): {
 
      // Fill coefficients with increasing values
      int64_t size = ncumcoeffs();
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs_[i] = torch::linspace(
            std::pow(10, i) * 0, std::pow(10, i) * (size - 1), size, options_);
 
      break;
    }
 
    default:
      throw std::runtime_error("Unsupported init option");
    }
  }
 
protected:
  inline void update_coeffs(const utils::TensorArray<parDim_> &knots,
                            const utils::TensorArray<parDim_> &knot_indices) {
 
    // Check compatibility of arguments
    for (short_t i = 0; i < parDim_; ++i)
      assert(knots[i].numel() == knot_indices[i].numel() + degrees_[i] + 1);
 
    if constexpr (parDim_ == 1) {
 
      auto basfunc = update_coeffs_univariate<degrees_[0], 0>(
          knots[0].flatten(), knot_indices[0].flatten());
 
      auto coeff_indices = find_coeff_indices(knot_indices);
 
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs(i) =
            utils::dotproduct(basfunc, coeffs(i)
                                           .index_select(0, coeff_indices)
                                           .view({-1, knot_indices[0].numel()}))
                .view(knot_indices[0].sizes());
 
    } else {
 
      // Lambda expressions to evaluate the basis functions
      auto basfunc_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        if constexpr (sizeof...(Is) == 1)
          return (update_coeffs_univariate<degrees_[Is], Is>(
                      knots[Is].flatten(), knot_indices[Is].flatten()),
                  ...);
        else
          return utils::kron(update_coeffs_univariate<degrees_[Is], Is>(
              knots[Is].flatten(), knot_indices[Is].flatten())...);
      };
 
      auto basfunc = basfunc_(utils::make_reverse_index_sequence<parDim_>{});
 
      // Lambda expression to calculate the partial product of array
      // entry from start_index to stop_index (including the latter)
      auto prod_ = [](utils::TensorArray<parDim_> array, short_t start_index,
                      short_t stop_index) {
        int64_t result{1};
        for (short_t i = start_index; i <= stop_index; ++i)
          result *= array[i].numel();
        return result;
      };
 
      utils::TensorArray<parDim_> knot_indices_;
 
      for (short_t i = 0; i < parDim_; ++i)
        knot_indices_[i] =
            knot_indices[i]
                .repeat_interleave(prod_(knot_indices, 0, i - 1), 0)
                .repeat(prod_(knot_indices, i + 1, parDim_ - 1));
 
      auto coeff_indices = find_coeff_indices(knot_indices_);
 
      for (short_t i = 0; i < geoDim_; ++i)
        coeffs(i) = utils::dotproduct(basfunc,
                                      coeffs(i)
                                          .index_select(0, coeff_indices)
                                          .view({-1, knot_indices_[0].numel()}))
                        .view(knot_indices_[0].sizes());
    }
  }
 
  //  clang-format off
  //  clang-format on
  template <short_t degree, short_t dim, short_t deriv>
  inline auto eval_basfunc_univariate(const torch::Tensor &xi,
                                      const torch::Tensor &knot_indices) const {
    assert(xi.sizes() == knot_indices.sizes());
 
    if constexpr (deriv > degree) {
      return torch::zeros({degree + 1, xi.numel()}, options_);
    } else {
      // Algorithm 2.22 from \cite Lyche:2011
      torch::Tensor b = torch::ones({xi.numel()}, options_);
 
      // Calculate R_k, k = 1, ..., p_d-r_d
      for (short_t k = 1; k <= degree - deriv; ++k) {
 
        // Instead of calculating t1 and t2 we calculate t1 and t21=(t2-t1)
        auto t1 =
            knots_[dim].index_select(0, utils::VSlice(knot_indices, -k + 1, 1));
        auto t21 =
            knots_[dim].index_select(0, utils::VSlice(knot_indices, 1, k + 1)) -
            t1;
 
        // We handle the special case 0/0:=0 by first creating a
        // mask that is 1 if t2-t1 < eps and 0 otherwise. Note that
        // we do not have to take the absolute value as t2 >= t1.
        auto mask = (t21 < std::numeric_limits<real_t>::epsilon())
                        .to(::iganet::dtype<real_t>());
 
        // Instead of computing (xi-t1)/(t2-t1) which is prone to
        // yielding 0/0 we compute (xi-t1-mask)/(t2-t1-mask) which
        // equals the original expression if the mask is 0, i.e.,
        // t2-t1 >= eps and 1 otherwise since t1 <= xi < t2.
        auto w = torch::div(xi.repeat(k) - t1 - mask, t21 - mask);
 
        // Calculate the vector of B-splines evaluated at xi
        b = torch::cat({torch::mul(torch::ones_like(w, options_) - w, b),
                        torch::zeros_like(xi, options_)},
                       0) +
            torch::cat({torch::zeros_like(xi, options_), torch::mul(w, b)}, 0);
      }
 
      // Calculate DR_k, k = p_d-r_d+1, ..., p_d
      for (short_t k = degree - deriv + 1; k <= degree; ++k) {
 
        // Instead of calculating t1 and t2 we calculate t1 and t21=(t2-t1)
        auto t21 =
            knots_[dim].index_select(0, utils::VSlice(knot_indices, 1, k + 1)) -
            knots_[dim].index_select(0, utils::VSlice(knot_indices, -k + 1, 1));
 
        // We handle the special case 0/0:=0 by first creating a
        // mask that is 1 if t2-t1 < eps and 0 otherwise. Note that
        // we do not have to take the absolute value as t2 >= t1.
        auto mask = (t21 < std::numeric_limits<real_t>::epsilon())
                        .to(::iganet::dtype<real_t>());
 
        // Instead of computing 1/(t2-t1) which is prone to yielding
        // 0/0 we compute (1-mask)/(t2-t1-mask) which equals the
        // original expression if the mask is 0, i.e., t2-t1 >= eps
        // and 1 otherwise since t1 <= xi < t2.
        auto w = torch::div(torch::ones_like(t21, options_) - mask, t21 - mask);
 
        // Calculate the vector of B-splines evaluated at xi
        b = torch::cat({torch::mul(-w, b), torch::zeros_like(xi, options_)},
                       0) +
            torch::cat({torch::zeros_like(xi, options_), torch::mul(w, b)}, 0);
      }
 
      return b.view({degree + 1, xi.numel()});
    }
  }
 
  template <short_t degree, short_t dim, short_t deriv>
  inline auto eval_basfunc_univariate_tr(const torch::Tensor &xi,
                                         const torch::Tensor &knot_indices) const {
    assert(xi.sizes() == knot_indices.sizes());
    
    if constexpr (deriv > degree) {
      return torch::zeros({xi.numel(), degree + 1}, options_);  // swapped shape
    } else {
      torch::Tensor b = torch::ones({xi.numel()}, options_);
      
      // Calculate R_k, k = 1, ..., degree-deriv
      for (short_t k = 1; k <= degree - deriv; ++k) {
        auto t1 = knots_[dim].index_select(0, utils::VSlice(knot_indices, -k + 1, 1));
        auto t21 = knots_[dim].index_select(0, utils::VSlice(knot_indices, 1, k + 1)) - t1;
        
        auto mask = (t21 < std::numeric_limits<real_t>::epsilon())
          .to(::iganet::dtype<real_t>());
        
        auto w = torch::div(xi.repeat(k) - t1 - mask, t21 - mask);
        
        b = torch::cat({torch::mul(torch::ones_like(w, options_) - w, b),
            torch::zeros_like(xi, options_)},
          0) +
          torch::cat({torch::zeros_like(xi, options_), torch::mul(w, b)}, 0);
      }
      
      // Calculate DR_k, k = degree-deriv+1, ..., degree
      for (short_t k = degree - deriv + 1; k <= degree; ++k) {
        auto t21 = knots_[dim].index_select(0, utils::VSlice(knot_indices, 1, k + 1)) -
          knots_[dim].index_select(0, utils::VSlice(knot_indices, -k + 1, 1));
        
        auto mask = (t21 < std::numeric_limits<real_t>::epsilon())
          .to(::iganet::dtype<real_t>());
        
        auto w = torch::div(torch::ones_like(t21, options_) - mask, t21 - mask);
        
        b = torch::cat({torch::mul(-w, b), torch::zeros_like(xi, options_)},
                       0) +
          torch::cat({torch::zeros_like(xi, options_), torch::mul(w, b)}, 0);
      }
      
      // Swap axes: shape [degree+1, xi.numel()] → [xi.numel(), degree+1]
      return b.view({degree + 1, xi.numel()}).transpose(0, 1);
    }
  }
 
  template <short_t degree, short_t dim>
  inline auto
  update_coeffs_univariate(const torch::Tensor &knots,
                           const torch::Tensor &knot_indices) const {
    // Algorithm 2.22 from \cite Lyche:2011 modified to implement
    // the Oslo algorithm (Algorithm 4.11 from \cite Lyche:2011)
    torch::Tensor b = torch::ones({knot_indices.numel()}, options_);
 
    // Calculate R_k, k = 1, ..., p_d
    for (short_t k = 1; k <= degree; ++k) {
 
      // Instead of calculating t1 and t2 we calculate t1 and t21=(t2-t1)
      auto t1 =
          knots_[dim].index_select(0, utils::VSlice(knot_indices, -k + 1, 1));
      auto t21 =
          knots_[dim].index_select(0, utils::VSlice(knot_indices, 1, k + 1)) -
          t1;
 
      // We handle the special case 0/0:=0 by first creating a
      // mask that is 1 if t2-t1 < eps and 0 otherwise. Note that
      // we do not have to take the absolute value as t2 >= t1.
      auto mask = (t21 < std::numeric_limits<real_t>::epsilon())
                      .to(::iganet::dtype<real_t>());
 
      // Instead of computing (xi-t1)/(t2-t1) which is prone to
      // yielding 0/0 we compute (xi-t1-mask)/(t2-t1-mask) which
      // equals the original expression if the mask is 0, i.e.,
      // t2-t1 >= eps and 1 otherwise since t1 <= xi < t2.
      auto w = torch::div(
          knots.index({torch::indexing::Slice(k, knot_indices.numel() + k)})
                  .repeat(k) -
              t1 - mask,
          t21 - mask);
 
      // Calculate the vector of B-splines evaluated at xi
      b = torch::cat({torch::mul(torch::ones_like(w, options_) - w, b),
                      torch::zeros_like(knot_indices, options_)},
                     0) +
          torch::cat(
              {torch::zeros_like(knot_indices, options_), torch::mul(w, b)}, 0);
    }
 
    return b.view({degree + 1, knot_indices.numel()});
  }
 
public:
  auto to_gismo() const {
 
#ifdef IGANET_WITH_GISMO
 
    gismo::gsMatrix<real_t> coefs(ncumcoeffs(), geoDim_);
 
    for (short_t g = 0; g < geoDim_; ++g) {
      auto [coeffs_cpu, coeffs_accessor] =
          utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
      auto coeffs_cpu_ptr = coeffs_cpu.template data_ptr<real_t>();
      coefs.col(g) =
          gismo::gsAsConstVector<real_t>(coeffs_cpu_ptr, coeffs_cpu.size(0));
    }
 
    std::array<gismo::gsKnotVector<real_t>, parDim_> kv;
 
    for (short_t i = 0; i < parDim_; ++i) {
      auto [knots_cpu, knots_accessor] =
          utils::to_tensorAccessor<real_t, 1>(knots_[i], torch::kCPU);
      auto knots_cpu_ptr = knots_cpu.template data_ptr<real_t>();
      kv[i] = gismo::gsKnotVector<real_t>(degrees_[i], knots_cpu_ptr,
                                          knots_cpu_ptr + knots_cpu.size(0));
    }
 
    if constexpr (parDim_ == 1) {
 
      return gismo::gsBSpline<real_t>(gismo::give(kv[0]), gismo::give(coefs));
 
    } else if constexpr (parDim_ == 2) {
 
      return gismo::gsTensorBSpline<parDim_, real_t>(
          gismo::give(kv[0]), gismo::give(kv[1]), gismo::give(coefs));
 
    } else if constexpr (parDim_ == 3) {
 
      return gismo::gsTensorBSpline<parDim_, real_t>(
          gismo::give(kv[0]), gismo::give(kv[1]), gismo::give(kv[2]),
          gismo::give(coefs));
 
    } else if constexpr (parDim_ == 4) {
 
      return gismo::gsTensorBSpline<parDim_, real_t>(
          gismo::give(kv[0]), gismo::give(kv[1]), gismo::give(kv[2]),
          gismo::give(kv[3]), gismo::give(coefs));
 
    } else
      throw std::runtime_error("Invalid parametric dimension");
 
#else
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_GISMO turned on");
#endif
  }
 
#ifdef IGANET_WITH_GISMO
 
  // @brief Updates a given gsBSpline object from the B-spline object
  gismo::gsBSpline<real_t> &to_gismo(gismo::gsBSpline<real_t> &bspline,
                                     bool updateKnotVector = true,
                                     bool updateCoeffs = true) const {
 
    if (updateKnotVector) {
 
      if constexpr (parDim_ == 1) {
 
        if (bspline.degree(0) != degrees_[0])
          throw std::runtime_error("Degrees mismatch");
 
        auto [knots_cpu, knots_accessor] =
            utils::to_tensorAccessor<real_t, 1>(knots_[0], torch::kCPU);
        auto knots_cpu_ptr = knots_cpu.template data_ptr<real_t>();
 
        gismo::gsKnotVector<real_t> kv(degrees_[0], knots_cpu_ptr,
                                       knots_cpu_ptr + knots_cpu.size(0));
 
        bspline.knots(0).swap(kv);
 
      } else
        throw std::runtime_error("Invalid parametric dimension");
    }
 
    if (updateCoeffs) {
 
      for (short_t g = 0; g < geoDim_; ++g) {
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
        auto coeffs_cpu_ptr = coeffs_cpu.template data_ptr<real_t>();
        bspline.coefs().col(g) =
            gismo::gsAsConstVector<real_t>(coeffs_cpu_ptr, coeffs_cpu.size(0));
      }
    }
 
    return bspline;
  }
 
  // @brief Updates a given gsTensorBSpline object from the B-spline object
  gismo::gsTensorBSpline<parDim_, real_t> &
  to_gismo(gismo::gsTensorBSpline<parDim_, real_t> &bspline,
           bool updateKnotVector = true, bool updateCoeffs = true) const {
 
    if (updateKnotVector) {
 
      // Check compatibility of arguments
      for (short_t i = 0; i < parDim_; ++i)
        assert(bspline.degree(i) == degrees_[i]);
 
      for (short_t i = 0; i < parDim_; ++i) {
        auto [knots_cpu, knots_accessor] =
            utils::to_tensorAccessor<real_t, 1>(knots_[i], torch::kCPU);
        auto knots_cpu_ptr = knots_cpu.template data_ptr<real_t>();
 
        gismo::gsKnotVector<real_t> kv(degrees_[i], knots_cpu_ptr,
                                       knots_cpu_ptr + knots_cpu.size(0));
        bspline.knots(i).swap(kv);
      }
    }
 
    if (updateCoeffs) {
 
      for (short_t g = 0; g < geoDim_; ++g) {
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
        auto coeffs_cpu_ptr = coeffs_cpu.template data_ptr<real_t>();
        bspline.coefs().col(g) =
            gismo::gsAsConstVector<real_t>(coeffs_cpu_ptr, coeffs_cpu.size(0));
      }
    }
 
    return bspline;
  }
 
#else // IGANET_WITH_GISMO
 
  template <typename BSpline>
  BSpline &to_gismo(BSpline &bspline, bool, bool) const {
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_GISMO turned on");
    return bspline;
  }
 
#endif // IGANET_WITH_GISMO
 
#ifdef IGANET_WITH_GISMO
 
  // @brief Updates the B-spline object from a given gsBSpline object
  auto &from_gismo(const gismo::gsBSpline<real_t> &bspline,
                   bool updateCoeffs = true, bool updateKnotVector = false) {
 
    if (updateKnotVector) {
 
      throw std::runtime_error(
          "Knot vectors can only be updated for Non-uniform B-splines");
    }
 
    if (updateCoeffs) {
 
      if (bspline.coefs().cols() != geoDim_)
        throw std::runtime_error("Geometric dimensions mismatch");
 
      if (bspline.coefs().rows() != ncumcoeffs())
        throw std::runtime_error("Coefficient vector dimensions mismatch");
 
      for (short_t g = 0; g < geoDim_; ++g) {
 
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
 
        const real_t *coeffs_ptr = bspline.coefs().col(g).data();
 
        for (int64_t i = 0; i < ncoeffs_[g]; ++i)
          coeffs_accessor[i] = coeffs_ptr[i];
 
        coeffs_[g] = coeffs_[g].to(options_.device());
      }
    }
 
    return *this;
  }
 
  // @brief Updates the B-spline object from a given gsTensorBSpline object
  auto &from_gismo(const gismo::gsTensorBSpline<parDim_, real_t> &bspline,
                   bool updateCoeffs = true, bool updateKnotVector = false) {
 
    if (updateKnotVector) {
 
      throw std::runtime_error(
          "Knot vectors can only be updated for Non-uniform B-splines");
    }
 
    if (updateCoeffs) {
 
      if (bspline.coefs().cols() != geoDim_)
        throw std::runtime_error("Geometric dimensions mismatch");
 
      if (bspline.coefs().rows() != ncumcoeffs())
        throw std::runtime_error("Coefficient vector dimensions mismatch");
 
      for (short_t g = 0; g < geoDim_; ++g) {
 
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<real_t, 1>(coeffs_[g], torch::kCPU);
 
        const real_t *coeffs_ptr = bspline.coefs().col(g).data();
 
        for (int64_t i = 0; i < ncoeffs_[g]; ++i)
          coeffs_accessor[i] = coeffs_ptr[i];
 
        coeffs_[g] = coeffs_[g].to(options_.device());
      }
    }
 
    return *this;
  }
 
#else // IGANET_WITH_GISMO
 
  template <typename BSpline> auto &from_gismo(BSpline &bspline, bool, bool) {
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_GISMO turned on");
    return *this;
  }
 
#endif // IGANET_WITH_GISMO
};
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
inline torch::serialize::OutputArchive &
operator<<(torch::serialize::OutputArchive &archive,
           const UniformBSplineCore<real_t, GeoDim, Degrees...> &obj) {
  return obj.write(archive);
}
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
inline torch::serialize::InputArchive &
operator>>(torch::serialize::InputArchive &archive,
           UniformBSplineCore<real_t, GeoDim, Degrees...> &obj) {
  return obj.read(archive);
}
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
class NonUniformBSplineCore
    : public NonUniformSplineCore_,
      public UniformBSplineCore<real_t, GeoDim, Degrees...> {
private:
  using Base = UniformBSplineCore<real_t, GeoDim, Degrees...>;
 
public:
  using value_type = real_t;
 
  template <template <typename, short_t, short_t...> class BSpline,
            std::make_signed_t<short_t> degree_elevate = 0>
  using derived_type = BSpline<real_t, GeoDim, (Degrees + degree_elevate)...>;
 
  template <std::make_signed_t<short_t> degree_elevate = 0>
  using self_type =
      Base::template derived_type<NonUniformBSplineCore, degree_elevate>;
 
  template <typename other_t, short_t GeoDim_, short_t... Degrees_>
  using derived_self_type =
      NonUniformBSplineCore<other_t, GeoDim_, Degrees_...>;
 
  template <typename other_t>
  using real_derived_self_type =
      NonUniformBSplineCore<other_t, GeoDim, Degrees...>;
 
  inline static constexpr bool is_uniform() { return false; }
 
  inline static constexpr bool is_nonuniform() { return true; }
 
  using UniformBSplineCore<real_t, GeoDim, Degrees...>::UniformBSplineCore;
 
  explicit NonUniformBSplineCore(
      const std::array<std::vector<typename Base::value_type>, Base::parDim_>
          &kv,
      enum init init = init::greville,
      Options<real_t> options = Options<real_t>{})
      : Base(options) {
    init_knots(kv);
    Base::init_coeffs(init);
  }
 
  NonUniformBSplineCore(const std::array<std::vector<typename Base::value_type>,
                                         Base::parDim_> &kv,
                        const utils::TensorArray<Base::geoDim_> &coeffs,
                        bool clone = false,
                        Options<real_t> options = Options<real_t>{})
      : Base(options) {
    init_knots(kv);
 
    // Copy/clone coefficients
    if (clone)
      for (short_t i = 0; i < Base::geoDim_; ++i)
        Base::coeffs_[i] = coeffs[i]
                               .clone()
                               .to(options.requires_grad(false))
                               .requires_grad_(Base::options.requires_grad());
    else
      for (short_t i = 0; i < Base::geoDim_; ++i)
        Base::coeffs_[i] = coeffs[i];
  }
 
private:
  inline void init_knots(
      const std::array<std::vector<typename Base::value_type>, Base::parDim_>
          &kv) {
    for (short_t i = 0; i < Base::parDim_; ++i) {
 
      // Check that knot vector has enough (n+p+1) entries
      if (2 * Base::degrees_[i] > kv[i].size() - 2)
        throw std::runtime_error("Knot vector is too short for an open knot "
                                 "vector (n+p+1 > 2*(p+1))");
 
      Base::knots_[i] = utils::to_tensor(kv[i], Base::options_);
      Base::nknots_[i] = Base::knots_[i].size(0);
      Base::ncoeffs_[i] = Base::nknots_[i] - Base::degrees_[i] - 1;
      Base::ncoeffs_reverse_[i] = Base::ncoeffs_[i];
    }
    // Reverse ncoeffs
    std::reverse(Base::ncoeffs_reverse_.begin(), Base::ncoeffs_reverse_.end());
  }
 
public:
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const torch::Tensor &xi) const {
    return eval<deriv, memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const utils::TensorArray<Base::parDim_> &xi) const {
    if constexpr (Base::parDim_ == 0) {
      utils::BlockTensor<torch::Tensor, 1, Base::geoDim_> result;
      for (short_t i = 0; i < Base::geoDim_; ++i)
        if constexpr (deriv == deriv::func)
          result.set(i, Base::coeffs_[i]);
        else
          result.set(i, torch::zeros_like(Base::coeffs_[i]));
      return result;
    } else
      return Base::template eval<deriv, memory_optimized>(
          xi, find_knot_indices(xi));
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto
  eval(const utils::TensorArray<Base::parDim_> &xi,
       const utils::TensorArray<Base::parDim_> &knot_indices) const {
    if constexpr (Base::parDim_ == 0) {
      utils::BlockTensor<torch::Tensor, 1, Base::geoDim_> result;
      for (short_t i = 0; i < Base::geoDim_; ++i)
        if constexpr (deriv == deriv::func)
          result.set(i, Base::coeffs_[i]);
        else
          result.set(i, torch::zeros_like(Base::coeffs_[i]));
      return result;
    } else
      return Base::template eval<deriv, memory_optimized>(xi, knot_indices);
  }
 
  template <deriv deriv = deriv::func, bool memory_optimized = false>
  inline auto eval(const utils::TensorArray<Base::parDim_> &xi,
                   const utils::TensorArray<Base::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
    if constexpr (Base::parDim_ == 0) {
      utils::BlockTensor<torch::Tensor, 1, Base::geoDim_> result;
      for (short_t i = 0; i < Base::geoDim_; ++i)
        if constexpr (deriv == deriv::func)
          result.set(i, Base::coeffs_[i]);
        else
          result.set(i, torch::zeros_like(Base::coeffs_[i]));
      return result;
    } else
      return Base::template eval<deriv, memory_optimized>(xi, knot_indices,
                                                          coeff_indices);
  }
 
  inline auto find_knot_indices(const torch::Tensor &xi) const {
    if constexpr (Base::parDim_ == 0)
      return torch::zeros_like(Base::coeffs_[0]).to(torch::kInt64);
    else
      return find_knot_indices(utils::TensorArray1({xi}));
  }
 
  inline auto
  find_knot_indices(const utils::TensorArray<Base::parDim_> &xi) const {
 
    utils::TensorArray<Base::parDim_> indices;
    for (short_t i = 0; i < Base::parDim_; ++i) {
      auto nnz = Base::knots_[i].repeat({xi[i].numel(), 1}) >
                 xi[i].flatten().view({-1, 1});
      indices[i] =
          torch::remainder(std::get<1>(((nnz.cumsum(1) == 1) & nnz).max(1)) - 1,
                           Base::nknots_[i] - Base::degrees_[i] - 1)
              .view(xi[i].sizes());
    }
    return indices;
  }
 
  inline NonUniformBSplineCore &uniform_refine(int numRefine = 1,
                                               int dim = -1) {
    assert(numRefine > 0);
    assert(dim == -1 || (dim >= 0 && dim < Base::parDim_));
 
    // Update knot vectors, number of knots and coefficients
    std::array<int64_t, Base::parDim_> nknots, ncoeffs;
    utils::TensorArray<Base::parDim_> knots, knots_indices;
 
    for (short_t i = 0; i < Base::parDim_; ++i) {
      auto [kv_cpu, kv_accessor] =
          utils::to_tensorAccessor<typename Base::value_type, 1>(
              Base::knots_[i], torch::kCPU);
 
      std::vector<typename Base::value_type> kv;
      kv.reserve(Base::nknots_[i]);
      kv.push_back(kv_accessor[0]);
 
      for (int64_t j = 1; j < kv_accessor.size(0); ++j) {
 
        if ((dim == -1 || dim == i) && (kv_accessor[j - 1] < kv_accessor[j]))
          for (int refine = 1; refine < (2 << (numRefine - 1)); ++refine)
            kv.push_back(
                kv_accessor[j - 1] +
                static_cast<Base::value_type>(refine) /
                    static_cast<Base::value_type>(2 << (numRefine - 1)) *
                    (kv_accessor[j] - kv_accessor[j - 1]));
 
        kv.push_back(kv_accessor[j]);
      }
 
      knots[i] = utils::to_tensor(kv, Base::options_);
      nknots[i] = kv.size();
      ncoeffs[i] = nknots[i] - Base::degrees_[i] - 1;
    }
 
    // The updated knot vectors have lengths \f$m_d+p_d+1\f$, where
    // \f$m_d\f$ is the number of coefficients after the update. To
    // update the coefficients using the Oslo algorithm (Algorithm
    // 4.11 from \cite Lyche:2011) we need to neglect the last
    // \f$p_d+1\f$ knots in what follows
    for (short_t i = 0; i < Base::parDim_; ++i)
      knots_indices[i] = knots[i].index({torch::indexing::Slice(
          0, knots[i].numel() - Base::degrees_[i] - 1)});
 
    // Get indices of the first \f$m_d\f$ new knots relative to old
    // knot vectors
    auto new_knot_indices = find_knot_indices(knots_indices);
 
    // Update coefficient vector
    Base::update_coeffs(knots, new_knot_indices);
 
    // Swap old and new data
    knots.swap(Base::knots_);
    nknots.swap(Base::nknots_);
    ncoeffs.swap(Base::ncoeffs_);
 
    Base::ncoeffs_reverse_ = Base::ncoeffs_;
    std::reverse(Base::ncoeffs_reverse_.begin(), Base::ncoeffs_reverse_.end());
 
    return *this;
  }
 
  inline NonUniformBSplineCore &
  insert_knots(const utils::TensorArray<Base::parDim_> &knots) {
    std::array<int64_t, Base::parDim_> nknots(Base::nknots_);
    std::array<int64_t, Base::parDim_> ncoeffs(Base::ncoeffs_);
    utils::TensorArray<Base::parDim_> knots_, knots_indices;
 
    // Update number of knots and coefficients and generate new knot
    // vectors
    for (short_t i = 0; i < Base::parDim_; ++i) {
      nknots[i] += knots[i].numel();
      ncoeffs[i] += knots[i].numel();
      knots_[i] =
          std::get<0>(torch::sort(torch::cat({Base::knots_[i], knots[i]})));
    }
 
    // The updated knot vectors have lengths \f$m_d+p_d+1\f$, where
    // \f$m_d\f$ is the number of coefficients after the update. To
    // update the coefficients using the Oslo algorithm (Algorithm
    // 4.11 from \cite Lyche:2011) we need to neglect the last
    // \f$p_d+1\f$ knots in what follows
    for (short_t i = 0; i < Base::parDim_; ++i)
      knots_indices[i] = knots_[i].index({torch::indexing::Slice(
          0, knots_[i].numel() - Base::degrees_[i] - 1)});
 
    // Get indices of the first \f$m_d\f$ new knots relative to old
    // knot vectors
    auto new_knot_indices = find_knot_indices(knots_indices);
 
    // Update coefficient vector
    Base::update_coeffs(knots_, new_knot_indices);
 
    // Swap old and new data
    knots_.swap(Base::knots_);
    nknots.swap(Base::nknots_);
    ncoeffs.swap(Base::ncoeffs_);
 
    Base::ncoeffs_reverse_ = Base::ncoeffs_;
    std::reverse(Base::ncoeffs_reverse_.begin(), Base::ncoeffs_reverse_.end());
 
    return *this;
  }
 
  inline NonUniformBSplineCore &reduce_continuity(int numReduce = 1,
                                                  int dim = -1) {
    assert(numReduce > 0);
    assert(dim == -1 || (dim >= 0 && dim < Base::parDim_));
 
    // Update knot vectors, number of knots and coefficients
    std::array<int64_t, Base::parDim_> nknots, ncoeffs;
    utils::TensorArray<Base::parDim_> knots, knots_indices;
 
    for (short_t i = 0; i < Base::parDim_; ++i) {
      auto [kv_cpu, kv_accessor] =
          utils::to_tensorAccessor<typename Base::value_type, 1>(
              Base::knots_[i], torch::kCPU);
 
      std::vector<typename Base::value_type> kv;
      kv.reserve(Base::nknots_[i]);
      kv.push_back(kv_accessor[0]);
 
      for (int64_t j = 1; j < kv_accessor.size(0); ++j) {
 
        if ((dim == -1 || dim == i) && (kv_accessor[j - 1] < kv_accessor[j]) &&
            (kv_accessor[j] < kv_accessor[kv_accessor.size(0) - 1]))
          for (int reduce = 0; reduce < numReduce; ++reduce)
            kv.push_back(kv_accessor[j]);
 
        kv.push_back(kv_accessor[j]);
      }
 
      knots[i] = utils::to_tensor(kv, Base::options_);
      nknots[i] = kv.size();
      ncoeffs[i] = nknots[i] - Base::degrees_[i] - 1;
    }
 
    // The updated knot vectors have lengths \f$m_d+p_d+1\f$, where
    // \f$m_d\f$ is the number of coefficients after the update. To
    // update the coefficients using the Oslo algorithm (Algorithm
    // 4.11 from \cite Lyche:2011) we need to neglect the last
    // \f$p_d+1\f$ knots in what follows
    for (short_t i = 0; i < Base::parDim_; ++i)
      knots_indices[i] = knots[i].index({torch::indexing::Slice(
          0, knots[i].numel() - Base::degrees_[i] - 1)});
 
    // Get indices of the first \f$m_d\f$ new knots relative to old
    // knot vectors
    auto new_knot_indices = find_knot_indices(knots_indices);
 
    // Update coefficient vector
    Base::update_coeffs(knots, new_knot_indices);
 
    // Swap old and new data
    knots.swap(Base::knots_);
    nknots.swap(Base::nknots_);
    ncoeffs.swap(Base::ncoeffs_);
 
    Base::ncoeffs_reverse_ = Base::ncoeffs_;
    std::reverse(Base::ncoeffs_reverse_.begin(), Base::ncoeffs_reverse_.end());
 
    return *this;
  }
 
#ifdef IGANET_WITH_GISMO
 
  // @brief Updates the B-spline object from a given gsBSpline object
  auto &from_gismo(const gismo::gsBSpline<typename Base::value_type> &bspline,
                   bool updateCoeffs = true, bool updateKnotVector = false) {
 
    if (updateKnotVector) {
 
      if constexpr (Base::parDim_ == 1) {
 
        if (bspline.degree(0) != Base::degrees_[0])
          throw std::runtime_error("Degrees mismatch");
 
        if (bspline.knots(0).size() != Base::nknots_[0])
          throw std::runtime_error("Knot vector dimensions mismatch");
 
        auto [knots0_cpu, knots0_accessor] =
            utils::to_tensorAccessor<typename Base::value_type, 1>(
                Base::knots_[0], torch::kCPU);
 
        const typename Base::value_type *knots0_ptr =
            bspline.knots(0).asMatrix().data();
 
        for (int64_t i = 0; i < Base::nknots_[0]; ++i)
          knots0_accessor[i] = knots0_ptr[i];
 
        Base::knots_[0] = Base::knots_[0].to(Base::options_.device());
 
      } else
        throw std::runtime_error("Invalid parametric dimension");
    }
 
    if (updateCoeffs) {
 
      if (bspline.coefs().rows() != Base::geoDim_)
        throw std::runtime_error("Geometric dimensions mismatch");
 
      if (bspline.coefs().cols() != Base::ncumcoeffs())
        throw std::runtime_error("Coefficient vector dimensions mismatch");
 
      for (short_t g = 0; g < Base::geoDim_; ++g) {
 
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<typename Base::value_type, 1>(
                Base::coeffs_[g], torch::kCPU);
 
        const typename Base::value_type *coeffs_ptr =
            bspline.coefs().row(g).data();
 
        for (int64_t i = 0; i < Base::ncoeffs_[g]; ++i)
          coeffs_accessor[i] = coeffs_ptr[i];
 
        Base::coeffs_[g] = Base::coeffs_[g].to(Base::options_.device());
      }
    }
 
    return *this;
  }
 
  // @brief Updates the B-spline object from a given gsTensorBSpline object
  auto &from_gismo(
      const gismo::gsTensorBSpline<Base::parDim_, typename Base::value_type>
          &bspline,
      bool updateCoeffs = true, bool updateKnotVector = false) {
 
    if (updateKnotVector) {
 
      for (short_t i = 0; i < Base::parDim_; ++i) {
        if (bspline.degree(i) != Base::degrees_[i])
          throw std::runtime_error("Degrees mismatch");
 
        if (bspline.knots(i).size() != Base::nknots_[i])
          throw std::runtime_error("Knot vector dimensions mismatch");
 
        auto [knots_cpu, knots_accessor] =
            utils::to_tensorAccessor<typename Base::value_type, 1>(
                Base::knots_[i], torch::kCPU);
 
        const typename Base::value_type *knots_ptr =
            bspline.knots(i).asMatrix().data();
 
        for (int64_t i = 0; i < Base::nknots_[i]; ++i)
          knots_accessor[i] = knots_ptr[i];
 
        Base::knots_[i] = Base::knots_[i].to(Base::options_.device());
      }
    }
 
    if (updateCoeffs) {
 
      if (bspline.coefs().rows() != Base::geoDim_)
        throw std::runtime_error("Geometric dimensions mismatch");
 
      if (bspline.coefs().cols() != Base::ncumcoeffs())
        throw std::runtime_error("Coefficient vector dimensions mismatch");
 
      for (short_t g = 0; g < Base::geoDim_; ++g) {
 
        auto [coeffs_cpu, coeffs_accessor] =
            utils::to_tensorAccessor<typename Base::value_type, 1>(
                Base::coeffs_[g], torch::kCPU);
 
        const typename Base::value_type *coeffs_ptr =
            bspline.coefs().row(g).data();
 
        for (int64_t i = 0; i < Base::ncoeffs_[g]; ++i)
          coeffs_accessor[i] = coeffs_ptr[i];
 
        Base::coeffs_[g] = Base::coeffs_[g].to(Base::options_.device());
      }
    }
 
    return *this;
  }
 
#else // IGANET_WITH_GISMO
 
  template <typename BSpline> auto &from_gismo(BSpline &bspline, bool, bool) {
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_GISMO turned on");
    return *this;
  }
 
#endif // IGANET_WITH_GISMO
};
 
class Spline_ {};
 
template <typename T>
concept SplineType = std::is_base_of_v<Spline_, T>;
 
template <typename T>
concept UniformSplineType =
    std::is_base_of_v<Spline_, T> && std::is_base_of_v<UniformSplineCore_, T> &&
    !std::is_base_of_v<NonUniformSplineCore_, T>;
 
template <typename T>
concept NonUniformSplineType = std::is_base_of_v<Spline_, T> &&
                               std::is_base_of_v<NonUniformSplineCore_, T>;
 
template <typename BSplineCore>
  requires SplineCoreType<BSplineCore>
class BSplineCommon : public Spline_,
                      public BSplineCore,
                      protected utils::FullQualifiedName {
public:
  using BSplineCore::BSplineCore;
 
  template <template <typename, short_t, short_t...> class T,
            std::make_signed_t<short_t> degree_elevate = 0>
  using derived_type = BSplineCommon<
      typename BSplineCore::template derived_type<T, degree_elevate>>;
 
  template <std::make_signed_t<short_t> degree_elevate = 0>
  using self_type =
      BSplineCommon<typename BSplineCore::template self_type<degree_elevate>>;
 
  template <typename real_t, short_t GeoDim, short_t... Degrees>
  using derived_self_type =
      BSplineCommon<typename BSplineCore::template derived_self_type<
          real_t, GeoDim, Degrees...>>;
 
  template <typename other_t>
  using real_derived_self_type = BSplineCommon<
      typename BSplineCore::template real_derived_self_type<other_t>>;
 
  using Ptr = std::shared_ptr<BSplineCommon>;
 
  using uPtr = std::unique_ptr<BSplineCommon>;
 
  BSplineCommon(const BSplineCommon &) = default;
 
  BSplineCommon(const BSplineCommon &other, bool clone) : BSplineCommon(other) {
    if (clone)
      for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
        BSplineCore::coeffs_[i] = other.coeffs(i).clone();
  }
 
  BSplineCommon(const BSplineCommon &other,
                const utils::TensorArray<BSplineCore::geoDim_> &coeffs,
                bool clone = false)
      : BSplineCommon(other) {
    if (clone)
      for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
        BSplineCore::coeffs_[i] = coeffs[i].clone();
    else
      for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
        BSplineCore::coeffs_[i] = coeffs[i];
  }
 
  BSplineCommon(BSplineCommon &&) = default;
 
  BSplineCommon(BSplineCommon &&other,
                utils::TensorArray<BSplineCore::geoDim_> &&coeffs)
      : BSplineCommon(std::move(other)) {
    for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
      BSplineCore::coeffs_[i] = std::move(coeffs[i]);
  }
 
  inline static Ptr
  make_unique(Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(options));
  }
 
  inline static Ptr
  make_unique(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              enum init init = init::greville,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(ncoeffs, init, options));
  }
 
  inline static Ptr
  make_unique(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              const utils::TensorArray<BSplineCore::geoDim_> &coeffs,
              bool clone = false,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(ncoeffs, coeffs, clone, options));
  }
 
  inline static Ptr
  make_unique(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              utils::TensorArray<BSplineCore::geoDim_> &&coeffs,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(ncoeffs, coeffs, options));
  }
 
  inline static Ptr
  make_unique(const std::array<std::vector<typename BSplineCore::value_type>,
                               BSplineCore::parDim_> &kv,
              enum init init = init::greville,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(kv, init, options));
  }
 
  inline static Ptr
  make_unique(const std::array<std::vector<typename BSplineCore::value_type>,
                               BSplineCore::parDim_> &kv,
              const utils::TensorArray<BSplineCore::geoDim_> &coeffs,
              bool clone = false,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return uPtr(new BSplineCommon(kv, coeffs, clone, options));
  }
 
  inline static Ptr
  make_shared(Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(options);
  }
 
  inline static Ptr
  make_shared(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              enum init init = init::greville,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(ncoeffs, init, options);
  }
 
  inline static Ptr
  make_shared(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              const utils::TensorArray<BSplineCore::geoDim_> &coeffs,
              bool clone = false,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(ncoeffs, coeffs, clone, options);
  }
 
  inline static Ptr
  make_shared(const std::array<int64_t, BSplineCore::parDim_> &ncoeffs,
              utils::TensorArray<BSplineCore::geoDim_> &&coeffs,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(ncoeffs, coeffs, options);
  }
 
  inline static Ptr
  make_shared(const std::array<std::vector<typename BSplineCore::value_type>,
                               BSplineCore::parDim_> &kv,
              enum init init = init::greville,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(kv, init, options);
  }
 
  inline static Ptr
  make_shared(const std::array<std::vector<typename BSplineCore::value_type>,
                               BSplineCore::parDim_> &kv,
              const utils::TensorArray<BSplineCore::geoDim_> &coeffs,
              bool clone = false,
              Options<typename BSplineCore::value_type> options =
                  Options<typename BSplineCore::value_type>{}) {
    return std::make_shared<BSplineCommon>(kv, coeffs, clone, options);
  }
 
  inline BSplineCommon &uniform_refine(int numRefine = 1, int dim = -1) {
    BSplineCore::uniform_refine(numRefine, dim);
    return *this;
  }
 
  inline auto clone() const {
    BSplineCommon result;
 
    result.nknots_ = BSplineCore::nknots_;
    result.ncoeffs_ = BSplineCore::ncoeffs_;
    result.ncoeffs_reverse_ = BSplineCore::ncoeffs_reverse_;
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      result.knots_[i] = BSplineCore::knots_[i].clone();
 
    for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
      result.coeffs_[i] = BSplineCore::coeffs_[i].clone();
 
    return result;
  }
 
  template <typename real_t> inline auto to(Options<real_t> options) const {
    BSplineCommon<typename BSplineCore::template real_derived_self_type<real_t>>
        result(options);
 
    result.nknots_ = BSplineCore::nknots_;
    result.ncoeffs_ = BSplineCore::ncoeffs_;
    result.ncoeffs_reverse_ = BSplineCore::ncoeffs_reverse_;
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      result.knots_[i] = BSplineCore::knots_[i].to(options);
 
    for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
      result.coeffs_[i] = BSplineCore::coeffs_[i].to(options);
 
    return result;
  }
 
  inline auto to(torch::Device device) const {
    BSplineCommon result(BSplineCore::options_.device(device));
 
    result.nknots_ = BSplineCore::nknots_;
    result.ncoeffs_ = BSplineCore::ncoeffs_;
    result.ncoeffs_reverse_ = BSplineCore::ncoeffs_reverse_;
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      result.knots_[i] = BSplineCore::knots_[i].to(device);
 
    for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
      result.coeffs_[i] = BSplineCore::coeffs_[i].to(device);
 
    return result;
  }
 
  template <typename real_t> inline auto to() const {
    return to(BSplineCore::options_.template dtype<real_t>());
  }
 
  inline auto diff(const BSplineCommon &other, int dim = -1) const {
    return this->clone().diff_(other, dim);
  }
 
  inline auto diff_(const BSplineCommon &other, int dim = -1) {
 
    bool compatible(true);
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      compatible *= (BSplineCore::nknots(i) == other.nknots(i));
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      compatible *= (BSplineCore::ncoeffs(i) == other.ncoeffs(i));
 
    if (!compatible)
      throw std::runtime_error("B-splines are not compatible");
 
    if (dim == -1) {
      for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
        BSplineCore::coeffs(i) -= other.coeffs(i);
    } else
      BSplineCore::coeffs(dim) -= other.coeffs(dim);
 
    return *this;
  }
 
  inline auto abs_diff(const BSplineCommon &other, int dim = -1) const {
    return this->clone().abs_diff_(other, dim);
  }
 
  inline auto abs_diff_(const BSplineCommon &other, int dim = -1) {
 
    bool compatible(true);
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      compatible *= (BSplineCore::nknots(i) == other.nknots(i));
 
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      compatible *= (BSplineCore::ncoeffs(i) == other.ncoeffs(i));
 
    if (!compatible)
      throw std::runtime_error("B-splines are not compatible");
 
    if (dim == -1) {
      for (short_t i = 0; i < BSplineCore::geoDim_; ++i)
        BSplineCore::coeffs(i) =
            torch::abs(BSplineCore::coeffs(i) - other.coeffs(i));
    } else
      BSplineCore::coeffs(dim) =
          torch::abs(BSplineCore::coeffs(dim) - other.coeffs(dim));
 
    return *this;
  }
 
  inline auto norm() const {
    return torch::mean(torch::pow(BSplineCore::eval(BSplineCore::greville())(0), 2));
  }
  
  inline auto scale(BSplineCore::value_type s, int dim = -1) const {
    return this->clone().scale_(s, dim);
  }
    
  inline auto scale_(BSplineCore::value_type s, int dim = -1) {
    if (dim == -1)
      for (int i = 0; i < BSplineCore::geoDim(); ++i)
        BSplineCore::coeffs(i) *= s;
    else
      BSplineCore::coeffs(dim) *= s;
    return *this;
  }
 
  inline auto
  scale(std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) const {
    return this->clone().scale_(v);
  }
  
  inline auto
  scale_(std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) {
    for (int i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) *= v[i];
    return *this;
  }
 
  inline auto translate(
                        std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) const {
    return this->clone().translate_(v);
  }
  
  inline auto translate_(
      std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) {
    for (int i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) += v[i];
    return *this;
  }
 
  inline auto rotate(BSplineCore::value_type angle) const {
    return this->clone().rotate_(angle);
  }
  
  inline auto rotate_(BSplineCore::value_type angle) {
 
    static_assert(BSplineCore::geoDim() == 2,
                  "Rotation about one angle is only available in 2D");
 
    utils::TensorArray<2> coeffs;
    coeffs[0] = std::cos(angle) * BSplineCore::coeffs(0) -
                std::sin(angle) * BSplineCore::coeffs(1);
    coeffs[1] = std::sin(angle) * BSplineCore::coeffs(0) +
                std::cos(angle) * BSplineCore::coeffs(1);
 
    BSplineCore::coeffs().swap(coeffs);
    return *this;
  }
 
  inline auto rotate(std::array<typename BSplineCore::value_type, 3> angle) const {
    return this->clone().rotate_(angle);
  }
  
  inline auto rotate_(std::array<typename BSplineCore::value_type, 3> angle) {
 
    static_assert(BSplineCore::geoDim() == 3,
                  "Rotation about two angles is only available in 3D");
 
    utils::TensorArray<3> coeffs;
    coeffs[0] =
        std::cos(angle[0]) * std::cos(angle[1]) * BSplineCore::coeffs(0) +
        (std::sin(angle[0]) * std::sin(angle[1]) * std::cos(angle[2]) -
         std::cos(angle[0]) * std::sin(angle[2])) *
            BSplineCore::coeffs(1) +
        (std::cos(angle[0]) * std::sin(angle[1]) * std::cos(angle[2]) +
         std::sin(angle[0]) * std::sin(angle[2])) *
            BSplineCore::coeffs(2);
 
    coeffs[1] =
        std::cos(angle[1]) * std::sin(angle[2]) * BSplineCore::coeffs(0) +
        (std::sin(angle[0]) * std::sin(angle[1]) * std::sin(angle[2]) +
         std::cos(angle[0]) * std::cos(angle[2])) *
            BSplineCore::coeffs(1) +
        (std::cos(angle[0]) * std::sin(angle[1]) * std::sin(angle[2]) -
         std::sin(angle[0]) * std::cos(angle[2])) *
            BSplineCore::coeffs(2);
 
    coeffs[2] =
        -std::sin(angle[1]) * BSplineCore::coeffs(0) +
        std::sin(angle[0]) * std::cos(angle[1]) * BSplineCore::coeffs(1) +
        std::cos(angle[0]) * std::cos(angle[1]) * BSplineCore::coeffs(2);
 
    BSplineCore::coeffs().swap(coeffs);
    return *this;
  }
 
  inline auto boundingBox() const {
 
    // Lambda expression to compute the minimum value of all dimensions
    auto min_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
      return torch::stack({BSplineCore::coeffs(Is).min()...});
    };
 
    // Lambda expression to compute the maximum value of all dimensions
    auto max_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
      return torch::stack({BSplineCore::coeffs(Is).max()...});
    };
 
    std::pair<torch::Tensor, torch::Tensor> bbox;
    bbox.first = min_(std::make_index_sequence<BSplineCore::geoDim_>{});
    bbox.second = max_(std::make_index_sequence<BSplineCore::geoDim_>{});
    return bbox;
  }
 
  template <bool memory_optimized = false>
  inline auto nv(const torch::Tensor &xi) const {
    return nv<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto nv(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    return nv<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false>
  inline auto
  nv(const utils::TensorArray<BSplineCore::parDim_> &xi,
     const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    return nv<memory_optimized>(
        xi, knot_indices,
        BSplineCore::template find_coeff_indices<memory_optimized>(
            knot_indices));
  }
 
  template <bool memory_optimized = false>
  inline auto nv(const utils::TensorArray<BSplineCore::parDim_> &xi,
                 const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                 const torch::Tensor &coeff_indices) const {
 
    if constexpr (BSplineCore::parDim_ == 1 && BSplineCore::geoDim_ == 2) {
      // Compute the perpendicular vector
      auto eval_ = BSplineCore::template eval<deriv::dx, memory_optimized>(
          xi, knot_indices, coeff_indices);
      return utils::BlockTensor<torch::Tensor, 1, 2>(*eval_[1], -*eval_[0]);
    } else if constexpr (BSplineCore::parDim_ == 1 &&
                         BSplineCore::geoDim_ == 3) {
      // Compute the Frenet normal vector
      auto t_ = BSplineCore::template eval<deriv::dx, memory_optimized>(
                    xi, knot_indices, coeff_indices)
                    .normalize();
      auto a_ = BSplineCore::template eval<deriv::dx ^ 2, memory_optimized>(
          xi, knot_indices, coeff_indices);
      auto n_ = a_ - a_.dot(t_) * t_;
      return utils::BlockTensor<torch::Tensor, 2, 3>(
          n_(0, 0), n_(0, 1), n_(0, 2), t_(0, 0), t_(0, 1), t_(0, 2));
    } else if constexpr (BSplineCore::parDim_ == 2 &&
                         BSplineCore::geoDim_ == 3) {
      // Compute the cross product of tangent vectors
      auto jac_ = jac<memory_optimized>(xi, knot_indices, coeff_indices);
      return utils::BlockTensor<torch::Tensor, 1, 3>(
          jac_(1, 0) * jac_(2, 1) - jac_(2, 0) * jac_(1, 1),
          jac_(2, 0) * jac_(0, 1) - jac_(0, 0) * jac_(2, 1),
          jac_(0, 0) * jac_(1, 1) - jac_(1, 0) * jac_(0, 1));
    } else {
      throw std::runtime_error("Unsupported parametric/geometric dimension");
      return utils::BlockTensor<torch::Tensor, 1, 1>{};
    }
  }
 
  //  clang-format off
  //  clang-format off
  template <bool memory_optimized = false>
  inline auto curl(const torch::Tensor &xi) const {
    return curl<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto curl(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    return curl<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto
  curl(const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    return curl<memory_optimized>(
        xi, knot_indices,
        BSplineCore::template find_coeff_indices<memory_optimized>(
            knot_indices));
  }
 
  template <bool memory_optimized = false>
  inline auto curl(const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    static_assert(BSplineCore::parDim_ == BSplineCore::geoDim_,
                  "curl(.) requires that parametric and geometric dimension "
                  "are the same");
 
    // Check compatibility of arguments
    for (short_t i = 0; i < BSplineCore::parDim_; ++i)
      assert(xi[i].sizes() == knot_indices[i].sizes());
    for (short_t i = 1; i < BSplineCore::parDim_; ++i)
      assert(xi[0].sizes() == xi[i].sizes());
 
    if constexpr (BSplineCore::parDim_ == 2)
 
      return utils::BlockTensor<torch::Tensor, 1, 1>(
          *BSplineCore::template eval<deriv::dx, memory_optimized>(
              xi, knot_indices, coeff_indices)[1] -
          *BSplineCore::template eval<deriv::dy, memory_optimized>(
              xi, knot_indices, coeff_indices)[0]);
 
    else if constexpr (BSplineCore::parDim_ == 3)
 
      return utils::BlockTensor<torch::Tensor, 1, 3>(
          *BSplineCore::template eval<deriv::dy, memory_optimized>(
              xi, knot_indices, coeff_indices)[2] -
              *BSplineCore::template eval<deriv::dz, memory_optimized>(
                  xi, knot_indices, coeff_indices)[1],
          *BSplineCore::template eval<deriv::dz, memory_optimized>(
              xi, knot_indices, coeff_indices)[0] +
              *BSplineCore::template eval<deriv::dx, memory_optimized>(
                  xi, knot_indices, coeff_indices)[2],
          *BSplineCore::template eval<deriv::dx, memory_optimized>(
              xi, knot_indices, coeff_indices)[1] +
              *BSplineCore::template eval<deriv::dy, memory_optimized>(
                  xi, knot_indices, coeff_indices)[0]);
 
    else {
      throw std::runtime_error("Unsupported parametric/geometric dimension");
      return utils::BlockTensor<torch::Tensor, 1, 1>{};
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto icurl(const Geometry &G, const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return icurl<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto icurl(const Geometry &G,
                    const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return icurl<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  icurl(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return icurl<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  icurl(const Geometry &G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const torch::Tensor &coeff_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
        const torch::Tensor &coeff_indices_G) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else {
      utils::BlockTensor<torch::Tensor, 1, 1> det;
      det[0] = std::make_shared<torch::Tensor>(torch::reciprocal(
          G.template jac<memory_optimized>(xi, knot_indices_G, coeff_indices_G)
              .det()));
 
      return det * (curl<memory_optimized>(xi, knot_indices, coeff_indices) *
                    G.template jac<memory_optimized>(xi, knot_indices_G,
                                                     coeff_indices_G));
    }
  }
 
  template <bool memory_optimized = false>
  inline auto div(const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    return div<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto div(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    return div<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false>
  inline auto
  div(const utils::TensorArray<BSplineCore::parDim_> &xi,
      const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    return div<memory_optimized>(
        xi, knot_indices,
        BSplineCore::template find_coeff_indices<memory_optimized>(
            knot_indices));
  }
 
  template <bool memory_optimized = false>
  inline auto div(const utils::TensorArray<BSplineCore::parDim_> &xi,
                  const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                  const torch::Tensor &coeff_indices) const {
 
    static_assert(BSplineCore::parDim_ == BSplineCore::geoDim_,
                  "div(.) requires parDim == geoDim");
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    // return torch::zeros_like(BSplineCore::coeffs_[0]);
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < BSplineCore::parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < BSplineCore::parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      // Lambda expression to evaluate the divergence
      auto div_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        return utils::BlockTensor<torch::Tensor, 1, 1>{
            (*BSplineCore::template eval<
                 static_cast<deriv>(utils::integer_pow<10, Is>::value),
                 memory_optimized>(xi, knot_indices, coeff_indices)[Is] +
             ...)};
      };
 
      return div_(std::make_index_sequence<BSplineCore::parDim_>{});
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto idiv(const Geometry &G, const torch::Tensor &xi) {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return idiv<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto idiv(const Geometry &G,
                   const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return idiv<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  idiv(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
       const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return idiv<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto idiv(const Geometry &G,
                   const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices,
                   const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
                   const torch::Tensor &coeff_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ijac<memory_optimized, Geometry>(G, xi, knot_indices,
                                              coeff_indices, knot_indices_G,
                                              coeff_indices_G)
          .trace();
  }
 
  template <bool memory_optimized = false>
  inline auto grad(const torch::Tensor &xi) const {
 
    static_assert(BSplineCore::geoDim_ == 1,
                  "grad(.) requires 1D variable, use jac(.) instead");
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return grad<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto grad(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
 
    static_assert(BSplineCore::geoDim_ == 1,
                  "grad(.) requires 1D variable, use jac(.) instead");
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return grad<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false>
  inline auto
  grad(const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
 
    static_assert(BSplineCore::geoDim_ == 1,
                  "grad(.) requires 1D variable, use jac(.) instead");
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return grad<memory_optimized>(
          xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices));
  }
 
  template <bool memory_optimized = false>
  inline auto grad(const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    static_assert(BSplineCore::geoDim_ == 1,
                  "grad(.) requires 1D variable, use jac(.) instead");
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < BSplineCore::parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < BSplineCore::parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      // Lambda expression to evaluate the gradient
      auto grad_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        return utils::BlockTensor<torch::Tensor, 1, BSplineCore::parDim_>{
            BSplineCore::template eval<
                static_cast<deriv>(utils::integer_pow<10, Is>::value),
                memory_optimized>(xi, knot_indices, coeff_indices)...};
      };
 
      return grad_(std::make_index_sequence<BSplineCore::parDim_>{});
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto igrad(const Geometry &G, const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return igrad<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto igrad(const Geometry &G,
                    const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return igrad<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  igrad(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return igrad<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  igrad(const Geometry &G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const torch::Tensor &coeff_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
        const torch::Tensor &coeff_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return grad<memory_optimized>(xi, knot_indices, coeff_indices) *
             G.template jac<memory_optimized>(xi, knot_indices_G,
                                              coeff_indices_G)
                 .ginv();
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto hess(const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return hess<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto hess(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return hess<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto
  hess(const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return hess<memory_optimized>(
          xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto hess(const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < BSplineCore::parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < BSplineCore::parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      // Lambda expression to evaluate the hessian
      auto hess_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        return utils::BlockTensor<torch::Tensor, BSplineCore::parDim_,
                                  BSplineCore::geoDim_, BSplineCore::parDim_>{
            BSplineCore::template eval<
                static_cast<deriv>(
                    utils::integer_pow<10, Is / BSplineCore::parDim_>::value) +
                    static_cast<deriv>(utils::integer_pow<
                                       10, Is % BSplineCore::parDim_>::value),
                memory_optimized>(xi, knot_indices, coeff_indices)...}
            .reorder_ikj();
      };
 
      return hess_(std::make_index_sequence<BSplineCore::parDim_ *
                                            BSplineCore::parDim_>{});
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ihess(const Geometry &G, const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ihess<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ihess(const Geometry &G,
                    const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ihess<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  ihess(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ihess<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  ihess(const Geometry &G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const torch::Tensor &coeff_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
        const torch::Tensor &coeff_indices_G) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else {
      utils::BlockTensor<torch::Tensor, BSplineCore::parDim_,
                         BSplineCore::parDim_, BSplineCore::geoDim_>
          hessu;
 
      auto hessG = G.template hess<memory_optimized>(xi, knot_indices_G,
                                                     coeff_indices_G);
      auto ijacG = ijac<memory_optimized>(G, xi, knot_indices, coeff_indices,
                                          knot_indices_G, coeff_indices_G);
 
      for (short_t component = 0; component < BSplineCore::geoDim_;
           ++component) {
        auto hess_component =
            hess<memory_optimized>(xi, knot_indices, coeff_indices)
                .slice(component);
 
        for (short_t k = 0; k < hessG.slices(); ++k) {
          hess_component -= ijacG(component, k) * hessG.slice(k);
        }
 
        auto jacInv = G.template jac<memory_optimized>(xi, knot_indices_G,
                                                       coeff_indices_G)
                          .ginv();
        auto hessu_component = jacInv.tr() * hess_component * jacInv;
 
        for (short_t i = 0; i < BSplineCore::parDim_; ++i)
          for (short_t j = 0; j < BSplineCore::parDim_; ++j)
            hessu.set(i, j, component, hessu_component(i, j));
      }
 
      return hessu;
    }
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto jac(const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return jac<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto jac(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return jac<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto
  jac(const utils::TensorArray<BSplineCore::parDim_> &xi,
      const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return jac<memory_optimized>(
          xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto jac(const utils::TensorArray<BSplineCore::parDim_> &xi,
                  const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                  const torch::Tensor &coeff_indices) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < BSplineCore::parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < BSplineCore::parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      // Lambda expression to evaluate the jacobian
      auto jac_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        return utils::BlockTensor<torch::Tensor, BSplineCore::parDim_,
                                  BSplineCore::geoDim_>{
            BSplineCore::template eval<
                static_cast<deriv>(utils::integer_pow<10, Is>::value),
                memory_optimized>(xi, knot_indices, coeff_indices)...}
            .tr();
      };
 
      return jac_(std::make_index_sequence<BSplineCore::parDim_>{});
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ijac(const Geometry &G, const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ijac<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ijac(const Geometry &G,
                   const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ijac<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  ijac(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
       const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ijac<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ijac(const Geometry &G,
                   const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices,
                   const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
                   const torch::Tensor &coeff_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return jac<memory_optimized>(xi, knot_indices, coeff_indices) *
             G.template jac<memory_optimized>(xi, knot_indices_G,
                                              coeff_indices_G)
                 .ginv();
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto lapl(const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return lapl<memory_optimized>(utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false>
  inline auto lapl(const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return lapl<memory_optimized>(xi, BSplineCore::find_knot_indices(xi));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto
  lapl(const utils::TensorArray<BSplineCore::parDim_> &xi,
       const utils::TensorArray<BSplineCore::parDim_> &knot_indices) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return lapl<memory_optimized>(
          xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices));
  }
 
  //  clang-format off
  //  clang-format on
  template <bool memory_optimized = false>
  inline auto lapl(const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
                   const torch::Tensor &coeff_indices) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
 
    else {
      // Check compatibility of arguments
      for (short_t i = 0; i < BSplineCore::parDim_; ++i)
        assert(xi[i].sizes() == knot_indices[i].sizes());
      for (short_t i = 1; i < BSplineCore::parDim_; ++i)
        assert(xi[0].sizes() == xi[i].sizes());
 
      // Lambda expression to evaluate the laplacian
      auto lapl_ = [&, this]<std::size_t... Is>(std::index_sequence<Is...>) {
        return utils::BlockTensor<torch::Tensor, 1, 1, BSplineCore::geoDim_>{
            (BSplineCore::template eval<
                 static_cast<deriv>(utils::integer_pow<10, Is>::value) ^ 2,
                 memory_optimized>(xi, knot_indices, coeff_indices) +
             ...)}
            .reorder_ikj();
      };
 
      return lapl_(std::make_index_sequence<BSplineCore::parDim_>{});
    }
  }
 
  template <bool memory_optimized = false, typename Geometry>
  auto ilapl(const Geometry &G, const torch::Tensor &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ilapl<memory_optimized, Geometry>(G, utils::TensorArray1({xi}));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto ilapl(const Geometry &G,
                    const utils::TensorArray<BSplineCore::parDim_> &xi) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ilapl<memory_optimized, Geometry>(
          G, xi, BSplineCore::find_knot_indices(xi), G.find_knot_indices(xi));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  ilapl(const Geometry G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G) const {
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
    else
      return ilapl<memory_optimized, Geometry>(
          G, xi, knot_indices,
          BSplineCore::template find_coeff_indices<memory_optimized>(
              knot_indices),
          knot_indices_G,
          G.template find_coeff_indices<memory_optimized>(knot_indices_G));
  }
 
  template <bool memory_optimized = false, typename Geometry>
  inline auto
  ilapl(const Geometry &G, const utils::TensorArray<BSplineCore::parDim_> &xi,
        const utils::TensorArray<BSplineCore::parDim_> &knot_indices,
        const torch::Tensor &coeff_indices,
        const utils::TensorArray<Geometry::parDim()> &knot_indices_G,
        const torch::Tensor &coeff_indices_G) const {
 
    if constexpr (BSplineCore::parDim_ == 0)
      return utils::BlockTensor<torch::Tensor, 1, 1>{
          torch::zeros_like(BSplineCore::coeffs_[0])};
 
    else {
      auto hessu =
          hess<memory_optimized>(xi, knot_indices, coeff_indices).slice(0);
 
      {
        auto igradG =
            igrad<memory_optimized>(G, xi, knot_indices, coeff_indices,
                                    knot_indices_G, coeff_indices_G);
        auto hessG = G.template hess<memory_optimized>(xi, knot_indices_G,
                                                       coeff_indices_G);
        assert(igradG.cols() == hessG.slices());
        for (short_t k = 0; k < hessG.slices(); ++k)
          hessu -= igradG(0, k) * hessG.slice(k);
      }
 
      auto jacInv =
          G.template jac<memory_optimized>(xi, knot_indices_G, coeff_indices_G)
              .ginv();
 
      return (jacInv.tr() * hessu * jacInv).trace();
    }
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot>
#else
  template <typename Backend = void>
#endif
  inline auto plot(const nlohmann::json &json = {}) const {
    return plot<Backend>(*this, json);
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot>
#else
  template <typename Backend = void>
#endif
  inline auto plot(const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const nlohmann::json &json = {}) const {
 
    return plot<Backend>(*this, xi, json);
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot>
#else
  template <typename Backend = void>
#endif
  inline auto plot(
      const std::initializer_list<utils::TensorArray<BSplineCore::parDim_>> &xi,
      const nlohmann::json &json = {}) const {
 
    return plot<Backend>(*this, xi, json);
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot,
            typename BSplineCoreColor>
#else
  template <typename Backend = void, typename BSplineCoreColor>
#endif
  inline auto plot(const BSplineCommon<BSplineCoreColor> &color,
                   const nlohmann::json &json = {}) const {
#ifdef IGANET_WITH_MATPLOT
    static_assert(BSplineCore::parDim() == BSplineCoreColor::parDim(),
                  "Parametric dimensions must match");
 
    if ((void *)this != (void *)&color && BSplineCoreColor::geoDim() > 1)
      throw std::runtime_error("BSpline for coloring must have geoDim=1");
 
    if constexpr (BSplineCore::parDim() == 1 && BSplineCore::geoDim() == 1) {
 
      //
      // mapping: [0,1] -> R^1
      //
 
      int64_t res0 = BSplineCore::ncoeffs(0);
      if (json.contains("res0"))
        res0 = json["res0"].get<int64_t>();
 
      // Create figure with specified backend
      auto f = matplot::figure<Backend>(false);
      f->backend()->run_command("unset warnings");
      f->ioff();
      auto ax = f->current_axes();
 
      // Create line
      auto Coords =
          BSplineCore::eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
      auto Coords_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(0), torch::kCPU);
      auto XAccessor = std::get<1>(Coords_cpu);
#else
      auto [Coords_cpu, XAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(0), torch::kCPU);
#endif
 
      matplot::vector_1d Xfine(res0, 0.0);
      matplot::vector_1d Yfine(res0, 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < res0; ++i)
        Xfine[i] = XAccessor[i];
 
      // Plot (colored) line
      if ((void *)this != (void *)&color) {
        if constexpr (BSplineCoreColor::geoDim_ == 1) {
 
          // Create colors
          auto Color =
              color.eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
          auto Color_cpu =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
          auto CAccessor = std::get<1>(Color_cpu);
#else
          auto [Color_cpu, CAccessor] =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
#endif
 
          matplot::vector_1d Cfine(res0, 0.0);
 
#pragma omp parallel for simd
          for (int64_t i = 0; i < res0; ++i)
            Cfine[i] = CAccessor[i];
 
          auto Cmin = *std::min_element(Cfine.begin(), Cfine.end());
          auto Cmax = *std::max_element(Cfine.begin(), Cfine.end());
 
          auto Cmap = matplot::colormap();
 
          auto a = Cmap.size() / (Cmax - Cmin);
          auto b = -a * Cmin;
 
          // Plot colored line
          ax->hold(matplot::on);
          for (std::size_t i = 0; i < Xfine.size() - 1; ++i)
            ax->plot({Xfine[i], Xfine[i + 1]}, {Yfine[i], Yfine[i + 1]})
                ->line_width(2)
                .color({Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][0],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][1],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][2]});
          ax->hold(matplot::off);
          matplot::colorbar(ax);
        } else
          throw std::runtime_error("BSpline for coloring must have geoDim=1");
      } else {
        // Plot unicolor line
        ax->plot(Xfine, Yfine, "b-")->line_width(2);
      }
 
      bool cnet = false;
      if (json.contains("cnet"))
        cnet = json["cnet"].get<bool>();
 
      if (cnet) {
        // Create control net
#ifdef __clang__
        auto coeffs_cpu =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
        auto xAccessor = std::get<1>(coeffs_cpu);
#else
        auto [coeffs_cpu, xAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
#endif
        matplot::vector_1d X(BSplineCore::ncoeffs(0), 0.0);
        matplot::vector_1d Y(BSplineCore::ncoeffs(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < BSplineCore::ncoeffs(0); ++i) {
          X[i] = xAccessor[i];
        }
 
        // Plot control net
        ax->hold(matplot::on);
        ax->plot(X, Y, ".k-")->line_width(1);
        ax->hold(matplot::off);
      }
 
      // Title
      if (json.contains("title"))
        ax->title(json["title"].get<std::string>());
      else
        ax->title("BSpline: [0,1] -> R");
 
      // X-axis label
      if (json.contains("xlabel"))
        ax->xlabel(json["xlabel"].get<std::string>());
      else
        ax->xlabel("x");
 
      // Y-axis label
      if (json.contains("ylabel"))
        ax->ylabel(json["ylabel"].get<std::string>());
      else
        ax->ylabel("y");
 
      return f;
    }
 
    else if constexpr (BSplineCore::parDim_ == 1 && BSplineCore::geoDim_ == 2) {
 
      //
      // mapping: [0,1] -> R^2
      //
 
      int64_t res0 = BSplineCore::ncoeffs(0);
      if (json.contains("res0"))
        res0 = json["res0"].get<int64_t>();
 
      // Create figure with specified backend
      auto f = matplot::figure<Backend>(false);
      f->backend()->run_command("unset warnings");
      f->ioff();
      auto ax = f->current_axes();
 
      // Create curve
      auto Coords =
          BSplineCore::eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
      auto Coords_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords, torch::kCPU);
      auto XAccessor = std::get<1>(Coords_cpu)[0];
      auto YAccessor = std::get<1>(Coords_cpu)[1];
#else
      auto [Coords0_cpu, XAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(0), torch::kCPU);
      auto [Coords1_cpu, YAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(1), torch::kCPU);
#endif
 
      matplot::vector_1d Xfine(res0, 0.0);
      matplot::vector_1d Yfine(res0, 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < res0; ++i) {
        Xfine[i] = XAccessor[i];
        Yfine[i] = YAccessor[i];
      }
 
      // Plot (colored) curve
      if ((void *)this != (void *)&color) {
        if constexpr (BSplineCoreColor::geoDim() == 1) {
 
          // Create colors
          auto Color =
              color.eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
          auto Color_cpu =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
          auto CAccessor = std::get<1>(Color_cpu);
#else
          auto [Color_cpu, CAccessor] =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
#endif
 
          matplot::vector_1d Cfine(res0, 0.0);
 
#pragma omp parallel for simd
          for (int64_t i = 0; i < res0; ++i) {
            Cfine[i] = CAccessor[i];
          }
 
          auto Cmin = *std::min_element(Cfine.begin(), Cfine.end());
          auto Cmax = *std::max_element(Cfine.begin(), Cfine.end());
 
          auto Cmap = matplot::colormap();
 
          auto a = Cmap.size() / (Cmax - Cmin);
          auto b = -a * Cmin;
 
          // Plot colored curve
          ax->hold(matplot::on);
          for (std::size_t i = 0; i < Xfine.size() - 1; ++i)
            ax->plot({Xfine[i], Xfine[i + 1]}, {Yfine[i], Yfine[i + 1]})
                ->line_width(2)
                .color({Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][0],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][1],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][2]});
          ax->hold(matplot::off);
          matplot::colorbar(ax);
        } else
          throw std::runtime_error("BSpline for coloring must have geoDim=1");
      } else {
        // Plot unicolor curve
        ax->plot(Xfine, Yfine, "b-")->line_width(2);
      }
 
      bool cnet = false;
      if (json.contains("cnet"))
        cnet = json["cnet"].get<bool>();
 
      if (cnet) {
        // Create control net
#ifdef __clang__
        auto coeffs_cpu =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(), torch::kCPU);
        auto xAccessor = std::get<1>(coeffs_cpu)[0];
        auto yAccessor = std::get<1>(coeffs_cpu)[1];
#else
        auto [coeffs0_cpu, xAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
        auto [coeffs1_cpu, yAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(1), torch::kCPU);
#endif
 
        matplot::vector_1d X(BSplineCore::ncoeffs(0), 0.0);
        matplot::vector_1d Y(BSplineCore::ncoeffs(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < BSplineCore::ncoeffs(0); ++i) {
          X[i] = xAccessor[i];
          Y[i] = yAccessor[i];
        }
 
        // Plot control net
        ax->hold(matplot::on);
        ax->plot(X, Y, ".k-")->line_width(1);
        ax->hold(matplot::off);
      }
 
      // Title
      if (json.contains("title"))
        ax->title(json["title"].get<std::string>());
      else
        ax->title("BSpline: [0,1] -> R^2");
 
      // X-axis label
      if (json.contains("xlabel"))
        ax->xlabel(json["xlabel"].get<std::string>());
      else
        ax->xlabel("x");
 
      // Y-axis label
      if (json.contains("ylabel"))
        ax->ylabel(json["ylabel"].get<std::string>());
      else
        ax->ylabel("y");
 
      return f;
    }
 
    else if constexpr (BSplineCore::parDim() == 1 &&
                       BSplineCore::geoDim() == 3) {
 
      //
      // mapping: [0,1] -> R^3
      //
 
      int64_t res0 = BSplineCore::ncoeffs(0);
      if (json.contains("res0"))
        res0 = json["res0"].get<int64_t>();
 
      // Create figure with specified backend
      auto f = matplot::figure<Backend>(false);
      f->backend()->run_command("unset warnings");
      f->ioff();
      auto ax = f->current_axes();
 
      auto Coords =
          BSplineCore::eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
      auto Coords_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords, torch::kCPU);
      auto XAccessor = std::get<1>(Coords_cpu)[0];
      auto YAccessor = std::get<1>(Coords_cpu)[1];
      auto ZAccessor = std::get<1>(Coords_cpu)[2];
#else
      auto [Coords0_cpu, XAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(0), torch::kCPU);
      auto [Coords1_cpu, YAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(1), torch::kCPU);
      auto [Coords2_cpu, ZAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              Coords(2), torch::kCPU);
#endif
 
      // Create curve
      matplot::vector_1d Xfine(res0, 0.0);
      matplot::vector_1d Yfine(res0, 0.0);
      matplot::vector_1d Zfine(res0, 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < res0; ++i) {
        Xfine[i] = XAccessor[i];
        Yfine[i] = YAccessor[i];
        Zfine[i] = ZAccessor[i];
      }
 
      // Plot (colored) curve
      if ((void *)this != (void *)&color) {
        if constexpr (BSplineCoreColor::geoDim() == 1) {
 
          auto Color =
              color.eval(torch::linspace(0, 1, res0, BSplineCore::options_));
#ifdef __clang__
          auto Color_cpu =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
          auto CAccessor = std::get<1>(Color_cpu);
#else
          auto [Color_cpu, CAccessor] =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       1>(Color(0), torch::kCPU);
#endif
 
          // Create colors
          matplot::vector_1d Cfine(matplot::vector_1d(res0, 0.0));
 
#pragma omp parallel for simd
          for (int64_t i = 0; i < res0; ++i) {
            Cfine[i] = CAccessor[i];
          }
 
          auto Cmin = *std::min_element(Cfine.begin(), Cfine.end());
          auto Cmax = *std::max_element(Cfine.begin(), Cfine.end());
 
          auto Cmap = matplot::colormap();
 
          auto a = Cmap.size() / (Cmax - Cmin);
          auto b = -a * Cmin;
 
          // Plot colored line
          ax->hold(matplot::on);
          for (std::size_t i = 0; i < Xfine.size() - 1; ++i)
            ax->plot3({Xfine[i], Xfine[i + 1]}, {Yfine[i], Yfine[i + 1]},
                      {Zfine[i], Zfine[i + 1]})
                ->line_width(2)
                .color({Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][0],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][1],
                        Cmap[a * (Cfine[i] + Cfine[i + 1]) / 2.0 - b][2]});
          ax->hold(matplot::off);
          matplot::colorbar(ax);
        } else
          throw std::runtime_error("BSpline for coloring must have geoDim=1");
      } else {
        // Plot curve
        ax->plot3(Xfine, Yfine, Zfine, "b-")->line_width(2);
      }
 
      bool cnet = false;
      if (json.contains("cnet"))
        cnet = json["cnet"].get<bool>();
 
      if (cnet) {
        // Create control net
#ifdef __clang__
        auto coeffs_cpu =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(), torch::kCPU);
        auto xAccessor = std::get<1>(coeffs_cpu)[0];
        auto yAccessor = std::get<1>(coeffs_cpu)[1];
        auto zAccessor = std::get<1>(coeffs_cpu)[2];
#else
        auto [coeffs0_cpu, xAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
        auto [coeffs1_cpu, yAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(1), torch::kCPU);
        auto [coeffs2_cpu, zAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(2), torch::kCPU);
#endif
 
        matplot::vector_1d X(BSplineCore::ncoeffs(0), 0.0);
        matplot::vector_1d Y(BSplineCore::ncoeffs(0), 0.0);
        matplot::vector_1d Z(BSplineCore::ncoeffs(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < BSplineCore::ncoeffs(0); ++i) {
          X[i] = xAccessor[i];
          Y[i] = yAccessor[i];
          Z[i] = zAccessor[i];
        }
 
        // Plot control net
        ax->hold(matplot::on);
        ax->plot3(X, Y, Z, ".k-")->line_width(1);
        ax->hold(matplot::off);
      }
 
      // Title
      if (json.contains("title"))
        ax->title(json["title"].get<std::string>());
      else
        ax->title("BSpline: [0,1] -> R^3");
 
      // X-axis label
      if (json.contains("xlabel"))
        ax->xlabel(json["xlabel"].get<std::string>());
      else
        ax->xlabel("x");
 
      // Y-axis label
      if (json.contains("ylabel"))
        ax->ylabel(json["ylabel"].get<std::string>());
      else
        ax->ylabel("y");
 
      // Z-axis label
      if (json.contains("zlabel"))
        ax->zlabel(json["zlabel"].get<std::string>());
      else
        ax->zlabel("z");
 
      return f;
    }
 
    else if constexpr (BSplineCore::parDim() == 2 &&
                       BSplineCore::geoDim() == 2) {
 
      //
      // mapping: [0,1]^2 -> R^2
      //
 
      int64_t res0 = BSplineCore::ncoeffs(0);
      int64_t res1 = BSplineCore::ncoeffs(1);
      if (json.contains("res0"))
        res0 = json["res0"].get<int64_t>();
      if (json.contains("res1"))
        res1 = json["res1"].get<int64_t>();
 
      // Create figure with specified backend
      auto f = matplot::figure<Backend>(false);
      f->backend()->run_command("unset warnings");
      f->ioff();
      auto ax = f->current_axes();
 
      // Create mesh
      utils::TensorArray<2> meshgrid = utils::to_array<2>(
          torch::meshgrid({torch::linspace(0, 1, res0, BSplineCore::options_),
                           torch::linspace(0, 1, res1, BSplineCore::options_)},
                          "xy"));
      auto Coords = BSplineCore::eval(meshgrid);
#ifdef __clang__
      auto Coords_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords, torch::kCPU);
      auto XAccessor = std::get<1>(Coords_cpu)[0];
      auto YAccessor = std::get<1>(Coords_cpu)[1];
#else
      auto [Coords0_cpu, XAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords(0), torch::kCPU);
      auto [Coords1_cpu, YAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords(1), torch::kCPU);
#endif
 
      matplot::vector_2d Xfine(res1, matplot::vector_1d(res0, 0.0));
      matplot::vector_2d Yfine(res1, matplot::vector_1d(res0, 0.0));
      matplot::vector_2d Zfine(res1, matplot::vector_1d(res0, 0.0));
 
#pragma omp parallel for simd collapse(2)
      for (int64_t i = 0; i < res0; ++i)
        for (int64_t j = 0; j < res1; ++j) {
          Xfine[j][i] = XAccessor[j][i];
          Yfine[j][i] = YAccessor[j][i];
        }
 
      // Plot (colored) mesh
      if ((void *)this != (void *)&color) {
        if constexpr (BSplineCoreColor::geoDim() == 1) {
 
          // Create colors
          auto Color = color.eval(meshgrid);
#ifdef __clang__
          auto Color_cpu =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       2>(Color, torch::kCPU);
          auto CAccessor = std::get<1>(Color_cpu)[0];
#else
          auto [Color0_cpu, CAccessor] =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       2>(Color(0), torch::kCPU);
#endif
 
          matplot::vector_2d Cfine(res1, matplot::vector_1d(res0, 0.0));
 
#pragma omp parallel for simd collapse(2)
          for (int64_t i = 0; i < res0; ++i)
            for (int64_t j = 0; j < res1; ++j)
              Cfine[j][i] = CAccessor[j][i];
 
          // Plot colored mesh
          matplot::view(2);
          ax->mesh(Xfine, Yfine, Cfine)->hidden_3d(false);
          matplot::colorbar(ax);
        } else
          throw std::runtime_error("BSpline for coloring must have geoDim=1");
      } else {
        // Plot unicolor mesh
        matplot::view(2);
        matplot::colormap(std::vector<std::vector<double>>{{ 0.0, 0.0, 1.0 }});
        ax->mesh(Xfine, Yfine, Zfine)->hidden_3d(false).line_width(2);
      }
 
      bool cnet = false;
      if (json.contains("cnet"))
        cnet = json["cnet"].get<bool>();
 
      if (cnet) {
        // Create control net
#ifdef __clang__
        auto coeffs_cpu =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(), torch::kCPU);
        auto xAccessor = std::get<1>(coeffs_cpu)[0];
        auto yAccessor = std::get<1>(coeffs_cpu)[1];
#else
        auto [coeffs0_cpu, xAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
        auto [coeffs1_cpu, yAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(1), torch::kCPU);
#endif
 
        matplot::vector_2d X(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
        matplot::vector_2d Y(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
        matplot::vector_2d Z(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
 
#pragma omp parallel for simd collapse(2)
        for (int64_t i = 0; i < BSplineCore::ncoeffs(0); ++i)
          for (int64_t j = 0; j < BSplineCore::ncoeffs(1); ++j) {
            X[j][i] = xAccessor[j * BSplineCore::ncoeffs(0) + i];
            Y[j][i] = yAccessor[j * BSplineCore::ncoeffs(0) + i];
          }
 
        // Plot control net
        ax->hold(matplot::on);
        ax->surf(X, Y, Z)
            ->palette_map_at_surface(true)
            .face_alpha(0)
            .line_width(1);
        for (std::size_t i = 0; i < X.size(); ++i)
          ax->scatter3(X[i], Y[i], Z[i], "k.");
        ax->hold(matplot::off);
      }
 
      // Title
      if (json.contains("title"))
        ax->title(json["title"].get<std::string>());
      else
        ax->title("BSpline: [0,1]^2 -> R^2");
 
      // X-axis label
      if (json.contains("xlabel"))
        ax->xlabel(json["xlabel"].get<std::string>());
      else
        ax->xlabel("x");
 
      // Y-axis label
      if (json.contains("ylabel"))
        ax->ylabel(json["ylabel"].get<std::string>());
      else
        ax->ylabel("y");
 
      // Z-axis label
      if (json.contains("zlabel"))
        ax->zlabel(json["zlabel"].get<std::string>());
      else
        ax->zlabel("z");
 
      return f;
    }
 
    else if constexpr (BSplineCore::parDim() == 2 &&
                       BSplineCore::geoDim() == 3) {
 
      // mapping: [0,1]^2 -> R^3
 
      int64_t res0 = BSplineCore::ncoeffs(0);
      int64_t res1 = BSplineCore::ncoeffs(1);
      if (json.contains("res0"))
        res0 = json["res0"].get<int64_t>();
      if (json.contains("res1"))
        res1 = json["res1"].get<int64_t>();
 
      // Create figure with specified backend
      auto f = matplot::figure<Backend>(false);
      f->backend()->run_command("unset warnings");
      f->ioff();
      auto ax = f->current_axes();
 
      // Create surface
      utils::TensorArray<2> meshgrid = utils::to_array<2>(
          torch::meshgrid({torch::linspace(0, 1, res0, BSplineCore::options_),
                           torch::linspace(0, 1, res1, BSplineCore::options_)},
                          "xy"));
      auto Coords = BSplineCore::eval(meshgrid);
#ifdef __clang__
      auto Coords_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords, torch::kCPU);
      auto XAccessor = std::get<1>(Coords_cpu)[0];
      auto YAccessor = std::get<1>(Coords_cpu)[1];
      auto ZAccessor = std::get<1>(Coords_cpu)[2];
#else
      auto [Coords0_cpu, XAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords(0), torch::kCPU);
      auto [Coords1_cpu, YAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords(1), torch::kCPU);
      auto [Coords2_cpu, ZAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 2>(
              Coords(2), torch::kCPU);
#endif
 
      matplot::vector_2d Xfine(res1, matplot::vector_1d(res0, 0.0));
      matplot::vector_2d Yfine(res1, matplot::vector_1d(res0, 0.0));
      matplot::vector_2d Zfine(res1, matplot::vector_1d(res0, 0.0));
 
#pragma omp parallel for simd collapse(2)
      for (int64_t i = 0; i < res0; ++i)
        for (int64_t j = 0; j < res1; ++j) {
          Xfine[j][i] = XAccessor[j][i];
          Yfine[j][i] = YAccessor[j][i];
          Zfine[j][i] = ZAccessor[j][i];
        }
 
      // Plot (colored) surface
      if ((void *)this != (void *)&color) {
        if constexpr (BSplineCoreColor::geoDim() == 1) {
 
          // Create colors
          auto Color = color.eval(meshgrid);
#ifdef __clang__
          auto Color_cpu =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       2>(Color, torch::kCPU);
          auto CAccessor = std::get<1>(Color_cpu)[0];
#else
          auto [Color_cpu, CAccessor] =
              utils::to_tensorAccessor<typename BSplineCoreColor::value_type,
                                       2>(Color(0), torch::kCPU);
#endif
 
          matplot::vector_2d Cfine(res1, matplot::vector_1d(res0, 0.0));
 
#pragma omp parallel for simd collapse(2)
          for (int64_t i = 0; i < res0; ++i)
            for (int64_t j = 0; j < res1; ++j) {
              Cfine[j][i] = CAccessor[j][i];
            }
 
          // Plot colored surface
          ax->mesh(Xfine, Yfine, Zfine, Cfine)->hidden_3d(false);
          matplot::colorbar(ax);
        } else
          throw std::runtime_error("BSpline for coloring must have geoDim=1");
      } else {
        // Plot unicolor surface
        matplot::colormap(std::vector<std::vector<double>>{{ 0.0, 0.0, 1.0 }});
        ax->mesh(Xfine, Yfine, Zfine)->hidden_3d(false).line_width(2);
      }
 
      bool cnet = false;
      if (json.contains("cnet"))
        cnet = json["cnet"].get<bool>();
 
      if (cnet) {
        // Create control net
#ifdef __clang__
        auto coeffs_cpu =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(), torch::kCPU);
        auto xAccessor = std::get<1>(coeffs_cpu)[0];
        auto yAccessor = std::get<1>(coeffs_cpu)[1];
        auto zAccessor = std::get<1>(coeffs_cpu)[2];
#else
        auto [coeffs0_cpu, xAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(0), torch::kCPU);
        auto [coeffs1_cpu, yAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(1), torch::kCPU);
        auto [coeffs2_cpu, zAccessor] =
            utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
                BSplineCore::coeffs(2), torch::kCPU);
#endif
 
        matplot::vector_2d X(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
        matplot::vector_2d Y(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
        matplot::vector_2d Z(BSplineCore::ncoeffs(1),
                             matplot::vector_1d(BSplineCore::ncoeffs(0), 0.0));
 
#pragma omp parallel for simd collapse(2)
        for (int64_t i = 0; i < BSplineCore::ncoeffs(0); ++i)
          for (int64_t j = 0; j < BSplineCore::ncoeffs(1); ++j) {
            X[j][i] = xAccessor[j * BSplineCore::ncoeffs(0) + i];
            Y[j][i] = yAccessor[j * BSplineCore::ncoeffs(0) + i];
            Z[j][i] = zAccessor[j * BSplineCore::ncoeffs(0) + i];
          }
 
        // Plot control net
        ax->hold(matplot::on);
        ax->surf(X, Y, Z)
            ->palette_map_at_surface(true)
            .face_alpha(0)
            .line_width(1);
        for (std::size_t i = 0; i < X.size(); ++i)
          ax->scatter3(X[i], Y[i], Z[i], "k.");
        ax->hold(matplot::off);
      }
 
      // Title
      if (json.contains("title"))
        ax->title(json["title"].get<std::string>());
      else
        ax->title("BSpline: [0,1]^2 -> R^3");
 
      // X-axis label
      if (json.contains("xlabel"))
        ax->xlabel(json["xlabel"].get<std::string>());
      else
        ax->xlabel("x");
 
      // Y-axis label
      if (json.contains("ylabel"))
        ax->ylabel(json["ylabel"].get<std::string>());
      else
        ax->ylabel("y");
 
      // Z-axis label
      if (json.contains("zlabel"))
        ax->zlabel(json["zlabel"].get<std::string>());
      else
        ax->zlabel("z");
 
      return f;
    }
 
    else
      throw std::runtime_error(
          "Unsupported combination of parametric/geometric dimensions");
#else
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_MATPLOT turned on");
#endif
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot,
            typename BSplineCoreColor>
#else
  template <typename Backend = void, typename BSplineCoreColor>
#endif
  inline auto plot(const BSplineCommon<BSplineCoreColor> &color,
                   const utils::TensorArray<BSplineCore::parDim_> &xi,
                   const nlohmann::json &json = {}) const {
 
#ifdef IGANET_WITH_MATPLOT
    auto f = plot<Backend>(color, json);
    auto ax = f->current_axes();
 
#ifdef __clang__
    auto xi_cpu =
        utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
            xi, torch::kCPU);
    auto xiAccessor = std::get<1>(xi_cpu);
#else
    auto [xi_cpu, xiAccessor] =
        utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
            xi, torch::kCPU);
#endif
 
    if constexpr (BSplineCore::parDim_ == 1) {
      matplot::vector_1d X(xi[0].size(0), 0.0);
      matplot::vector_1d Y(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < xi[0].size(0); ++i) {
        X[i] = xiAccessor[0][i];
      }
 
      ax->hold(matplot::on);
      ax->scatter(X, Y, ".");
      ax->hold(matplot::off);
    } else if constexpr (BSplineCore::parDim_ == 2) {
      matplot::vector_1d X(xi[0].size(0), 0.0);
      matplot::vector_1d Y(xi[0].size(0), 0.0);
      matplot::vector_1d Z(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < xi[0].size(0); ++i) {
        X[i] = xiAccessor[0][i];
        Y[i] = xiAccessor[1][i];
      }
 
      ax->hold(matplot::on);
      ax->scatter3(X, Y, Z, ".");
      ax->hold(matplot::off);
    } else if constexpr (BSplineCore::parDim_ == 3) {
      matplot::vector_1d X(xi[0].size(0), 0.0);
      matplot::vector_1d Y(xi[0].size(0), 0.0);
      matplot::vector_1d Z(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
      for (int64_t i = 0; i < xi[0].size(0); ++i) {
        X[i] = xiAccessor[0][i];
        Y[i] = xiAccessor[1][i];
        Z[i] = xiAccessor[2][i];
      }
 
      ax->hold(matplot::on);
      ax->scatter3(X, Y, Z, ".");
      ax->hold(matplot::off);
    } else
      throw std::runtime_error("Invalid parametric dimension");
 
    return f;
#else
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_MATPLOT turned on");
#endif
  }
 
#ifdef IGANET_WITH_MATPLOT
  template <typename Backend = matplot::backend::gnuplot,
            typename BSplineCoreColor>
#else
  template <typename Backend = void, typename BSplineCoreColor>
#endif
  inline auto plot(
      const BSplineCommon<BSplineCoreColor> &color,
      const std::initializer_list<utils::TensorArray<BSplineCore::parDim_>> &xi,
      const nlohmann::json &json = {}) const {
 
#ifdef IGANET_WITH_MATPLOT
    auto f = plot<Backend>(color, json);
    auto ax = f->current_axes();
 
    for (const auto &xi : xi) {
#ifdef __clang__
      auto xi_cpu =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              xi, torch::kCPU);
      auto xiAccessor = std::get<1>(xi_cpu);
#else
      auto [xi_cpu, xiAccessor] =
          utils::to_tensorAccessor<typename BSplineCoreColor::value_type, 1>(
              xi, torch::kCPU);
#endif
 
      if constexpr (BSplineCore::parDim_ == 1) {
        matplot::vector_1d X(xi[0].size(0), 0.0);
        matplot::vector_1d Y(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < xi[0].size(0); ++i) {
          X[i] = xiAccessor[0][i];
        }
 
        ax->hold(matplot::on);
        ax->scatter(X, Y, ".");
        ax->hold(matplot::off);
      } else if constexpr (BSplineCore::parDim_ == 2) {
        matplot::vector_1d X(xi[0].size(0), 0.0);
        matplot::vector_1d Y(xi[0].size(0), 0.0);
        matplot::vector_1d Z(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < xi[0].size(0); ++i) {
          X[i] = xiAccessor[0][i];
          Y[i] = xiAccessor[1][i];
        }
 
        ax->hold(matplot::on);
        ax->scatter3(X, Y, Z, ".");
        ax->hold(matplot::off);
      } else if constexpr (BSplineCore::parDim_ == 3) {
        matplot::vector_1d X(xi[0].size(0), 0.0);
        matplot::vector_1d Y(xi[0].size(0), 0.0);
        matplot::vector_1d Z(xi[0].size(0), 0.0);
 
#pragma omp parallel for simd
        for (int64_t i = 0; i < xi[0].size(0); ++i) {
          X[i] = xiAccessor[0][i];
          Y[i] = xiAccessor[1][i];
          Z[i] = xiAccessor[2][i];
        }
 
        ax->hold(matplot::on);
        ax->scatter3(X, Y, Z, ".");
        ax->hold(matplot::off);
 
      } else
        throw std::runtime_error("Invalid parametric dimension");
    }
    return f;
#else
    throw std::runtime_error(
        "This functions must be compiled with -DIGANET_WITH_MATPLOT turned on");
#endif
  }
 
  inline void pretty_print(std::ostream &os) const noexcept override {
    os << name() << "(\nparDim = " << BSplineCore::parDim()
       << ", geoDim = " << BSplineCore::geoDim() << ", degrees = ";
 
    for (short_t i = 0; i < BSplineCore::parDim() - 1; ++i)
      os << BSplineCore::degree(i) << "x";
    if (BSplineCore::parDim() > 0)
      os << BSplineCore::degree(BSplineCore::parDim() - 1);
    else
      os << 0;
 
    os << ", knots = ";
    for (short_t i = 0; i < BSplineCore::parDim() - 1; ++i)
      os << BSplineCore::nknots(i) << "x";
    if (BSplineCore::parDim() > 0)
      os << BSplineCore::nknots(BSplineCore::parDim() - 1);
    else
      os << 0;
 
    os << ", coeffs = ";
    for (short_t i = 0; i < BSplineCore::parDim() - 1; ++i)
      os << BSplineCore::ncoeffs(i) << "x";
    if (BSplineCore::parDim() > 0)
      os << BSplineCore::ncoeffs(BSplineCore::parDim() - 1);
    else
      os << 1;
 
    os << ", options = "
       << static_cast<torch::TensorOptions>(BSplineCore::options_);
 
    if (is_verbose(os)) {
      os << "\nknots [ ";
      for (const torch::Tensor &knots : BSplineCore::knots()) {
        os << (knots.is_view() ? "view/" : "owns/");
        os << (knots.is_contiguous() ? "cont " : "non-cont ");
      }
      if (BSplineCore::parDim() > 0)
        os << "] = " << BSplineCore::knots();
      else
        os << "] = {}";
 
      os << "\ncoeffs [ ";
      for (const torch::Tensor &coeffs : BSplineCore::coeffs()) {
        os << (coeffs.is_view() ? "view/" : "owns/");
        os << (coeffs.is_contiguous() ? "cont " : "non-cont ");
      }
      if (BSplineCore::ncumcoeffs() > 0)
        os << "] = " << BSplineCore::coeffs_view();
      else
        os << "] = {}";
    }
 
    os << "\n)";
  }
 
  BSplineCommon operator+(const BSplineCommon &other) const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) += other.coeffs(i);
 
    return result;
  }
 
  BSplineCommon operator-(const BSplineCommon &other) const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) -= other.coeffs(i);
 
    return result;
  }
 
  BSplineCommon operator*(BSplineCore::value_type s) const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) *= s;
 
    return result;
  }
 
  BSplineCommon operator*(
      std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v)
      const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) *= v[i];
 
    return result;
  }
 
  BSplineCommon operator/(BSplineCore::value_type s) const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) /= s;
 
    return result;
  }
 
  BSplineCommon operator/(
      std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v)
      const {
 
    BSplineCommon result{*this};
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      result.coeffs(i) /= v[i];
 
    return result;
  }
 
  BSplineCommon &operator+=(const BSplineCommon &other) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) += other.coeffs(i);
 
    return *this;
  }
 
  BSplineCommon &operator-=(const BSplineCommon &other) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) -= other.coeffs(i);
 
    return *this;
  }
 
  BSplineCommon &operator*=(BSplineCore::value_type s) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) *= s;
 
    return *this;
  }
 
  BSplineCommon &operator*=(
      std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) *= v[i];
 
    return *this;
  }
 
  BSplineCommon &operator/=(BSplineCore::value_type s) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) /= s;
 
    return *this;
  }
 
  BSplineCommon &operator/=(
      std::array<typename BSplineCore::value_type, BSplineCore::geoDim()> v) {
 
    for (short_t i = 0; i < BSplineCore::geoDim(); ++i)
      BSplineCore::coeffs(i) /= v[i];
 
    return *this;
  }
};
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
using UniformBSpline =
    BSplineCommon<UniformBSplineCore<real_t, GeoDim, Degrees...>>;
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
inline std::ostream &
operator<<(std::ostream &os,
           const UniformBSpline<real_t, GeoDim, Degrees...> &obj) {
  obj.pretty_print(os);
  return os;
}
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
using NonUniformBSpline =
    BSplineCommon<NonUniformBSplineCore<real_t, GeoDim, Degrees...>>;
 
template <typename real_t, short_t GeoDim, short_t... Degrees>
inline std::ostream &
operator<<(std::ostream &os,
           const NonUniformBSpline<real_t, GeoDim, Degrees...> &obj) {
  obj.pretty_print(os);
  return os;
}
} // namespace iganet