math.h

// Copyright 2024, UChicago Argonne, LLC
// All Rights Reserved
// Software Name: NEML2 -- the New Engineering material Model Library, version 2
// By: Argonne National Laboratory
// OPEN SOURCE LICENSE (MIT)
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.
 
#pragma once
 
#include "neml2/tensors/Tensor.h"
 
namespace neml2
{
class SR2;
class WR2;
class SWR4;
class SSR4;
class WSR4;
namespace math
{
constexpr Real eps = std::numeric_limits<at::scalar_value_type<Real>::type>::epsilon();
 
constexpr Real sqrt2 = 1.4142135623730951;
constexpr Real invsqrt2 = 0.7071067811865475;
 
constexpr Size mandel_reverse_index[3][3] = {{0, 5, 4}, {5, 1, 3}, {4, 3, 2}};
constexpr Size mandel_index[6][2] = {{0, 0}, {1, 1}, {2, 2}, {1, 2}, {0, 2}, {0, 1}};
 
constexpr Size skew_reverse_index[3][3] = {{0, 2, 1}, {2, 0, 0}, {1, 0, 0}};
constexpr Real skew_factor[3][3] = {{0.0, -1.0, 1.0}, {1.0, 0.0, -1.0}, {-1.0, 1.0, 0.0}};
 
inline constexpr Real
mandel_factor(Size i)
{
  return i < 3 ? 1.0 : sqrt2;
}
 
struct ConstantTensors
{
  ConstantTensors();
 
  // Get the global constants
  static ConstantTensors & get();
 
  static const torch::Tensor & full_to_mandel_map();
  static const torch::Tensor & mandel_to_full_map();
  static const torch::Tensor & full_to_mandel_factor();
  static const torch::Tensor & mandel_to_full_factor();
  static const torch::Tensor & full_to_skew_map();
  static const torch::Tensor & skew_to_full_map();
  static const torch::Tensor & full_to_skew_factor();
  static const torch::Tensor & skew_to_full_factor();
 
private:
  torch::Tensor _full_to_mandel_map;
  torch::Tensor _mandel_to_full_map;
  torch::Tensor _full_to_mandel_factor;
  torch::Tensor _mandel_to_full_factor;
  torch::Tensor _full_to_skew_map;
  torch::Tensor _skew_to_full_map;
  torch::Tensor _full_to_skew_factor;
  torch::Tensor _skew_to_full_factor;
};
 
Tensor full_to_reduced(const Tensor & full,
                       const torch::Tensor & rmap,
                       const torch::Tensor & rfactors,
                       Size dim = 0);
 
Tensor reduced_to_full(const Tensor & reduced,
                       const torch::Tensor & rmap,
                       const torch::Tensor & rfactors,
                       Size dim = 0);
 
Tensor full_to_mandel(const Tensor & full, Size dim = 0);
 
Tensor mandel_to_full(const Tensor & mandel, Size dim = 0);
 
Tensor full_to_skew(const Tensor & full, Size dim = 0);
 
Tensor skew_to_full(const Tensor & skew, Size dim = 0);
 
Tensor jacrev(const Tensor & y, const Tensor & p);
 
Tensor base_diag_embed(const Tensor & a, Size offset = 0, Size d1 = -2, Size d2 = -1);
 
SR2 skew_and_sym_to_sym(const SR2 & e, const WR2 & w);
 
SSR4 d_skew_and_sym_to_sym_d_sym(const WR2 & w);
 
SWR4 d_skew_and_sym_to_sym_d_skew(const SR2 & e);
 
WR2 multiply_and_make_skew(const SR2 & a, const SR2 & b);
 
WSR4 d_multiply_and_make_skew_d_first(const SR2 & b);
 
WSR4 d_multiply_and_make_skew_d_second(const SR2 & a);
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
batch_cat(const std::vector<T> & tensors, Size d = 0)
{
  neml_assert_dbg(!tensors.empty(), "batch_cat must be given at least one tensor");
  std::vector<torch::Tensor> torch_tensors(tensors.begin(), tensors.end());
  auto d2 = d >= 0 ? d : d - tensors.begin()->base_dim();
  return T(torch::cat(torch_tensors, d2), tensors.begin()->batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
neml2::Tensor
base_cat(const std::vector<T> & tensors, Size d = 0)
{
  neml_assert_dbg(!tensors.empty(), "base_cat must be given at least one tensor");
  std::vector<torch::Tensor> torch_tensors(tensors.begin(), tensors.end());
  auto d2 = d < 0 ? d : d + tensors.begin()->batch_dim();
  return neml2::Tensor(torch::cat(torch_tensors, d2), tensors.begin()->batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
batch_stack(const std::vector<T> & tensors, Size d = 0)
{
  neml_assert_dbg(!tensors.empty(), "batch_stack must be given at least one tensor");
  std::vector<torch::Tensor> torch_tensors(tensors.begin(), tensors.end());
  auto d2 = d >= 0 ? d : d - tensors.begin()->base_dim();
  return T(torch::stack(torch_tensors, d2), tensors.begin()->batch_dim() + 1);
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
neml2::Tensor
base_stack(const std::vector<T> & tensors, Size d = 0)
{
  neml_assert_dbg(!tensors.empty(), "base_stack must be given at least one tensor");
  std::vector<torch::Tensor> torch_tensors(tensors.begin(), tensors.end());
  auto d2 = d < 0 ? d : d + tensors.begin()->batch_dim();
  return neml2::Tensor(torch::stack(torch_tensors, d2), tensors.begin()->batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
batch_sum(const T & a, Size d = 0)
{
  neml_assert_dbg(a.batch_dim() > 0, "Must have a batch dimension to sum along");
  auto d2 = d >= 0 ? d : d - a.base_dim();
  return T(torch::sum(a, d2), a.batch_dim() - 1);
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
base_sum(const T & a, Size d = 0)
{
  auto d2 = d < 0 ? d : d + a.batch_dim();
  return T(torch::sum(a, d2), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
pow(const T & a, const Real & n)
{
  return T(torch::pow(a, n), a.batch_dim());
}
 
Tensor pow(const Real & a, const Tensor & n);
 
Tensor pow(const Tensor & a, const Tensor & n);
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
sign(const T & a)
{
  return T(torch::sign(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
cosh(const T & a)
{
  return T(torch::cosh(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
sinh(const T & a)
{
  return T(torch::sinh(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
tanh(const T & a)
{
  return T(torch::tanh(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
where(const torch::Tensor & condition, const T & a, const T & b)
{
  neml_assert_broadcastable_dbg(a, b);
  return T(torch::where(condition, a, b), broadcast_batch_dim(a, b));
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
heaviside(const T & a)
{
  return (sign(a) + 1.0) / 2.0;
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
macaulay(const T & a)
{
  return T(torch::Tensor(a) * torch::Tensor(heaviside(a)), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
dmacaulay(const T & a)
{
  return heaviside(a);
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
sqrt(const T & a)
{
  return T(torch::sqrt(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
exp(const T & a)
{
  return T(torch::exp(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
abs(const T & a)
{
  return T(torch::abs(a), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
diff(const T & a, Size n = 1, Size dim = -1)
{
  return T(torch::diff(a, n, dim), a.batch_dim());
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
batch_diag_embed(const T & a, Size offset = 0, Size d1 = -2, Size d2 = -1)
{
  return T(torch::diag_embed(
               a, offset, d1 < 0 ? d1 - a.base_dim() : d1, d2 < 0 ? d2 - a.base_dim() : d2),
           a.batch_dim() + 1);
}
 
template <class T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
T
log(const T & a)
{
  return T(torch::log(a), a.batch_dim());
}
 
namespace linalg
{
Tensor vector_norm(const Tensor & v);
 
Tensor inv(const Tensor & m);
 
Tensor solve(const Tensor & A, const Tensor & B);
 
std::tuple<Tensor, Tensor> lu_factor(const Tensor & A, bool pivot = true);
 
Tensor lu_solve(const Tensor & LU,
                const Tensor & pivots,
                const Tensor & B,
                bool left = true,
                bool adjoint = false);
} // namespace linalg
} // namespace math
} // namespace neml2