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// Copyright (c) 2010-2025, Lawrence Livermore National Security, LLC. Produced

// at the Lawrence Livermore National Laboratory. All Rights reserved. See files

// LICENSE and NOTICE for details. LLNL-CODE-806117.

//

// This file is part of the MFEM library. For more information and source code

// availability visit https://mfem.org.

//

// MFEM is free software; you can redistribute it and/or modify it under the

// terms of the BSD-3 license. We welcome feedback and contributions, see file

// CONTRIBUTING.md for details.


#include "../tmop.hpp"

#include "tmop_pa.hpp"

#include "../../general/forall.hpp"

#include "../../linalg/kernels.hpp"

#include "../../linalg/dinvariants.hpp"


namespace mfem

{


using Args = kernels::InvariantsEvaluator2D::Buffers;


static MFEM_HOST_DEVICE inline

void EvalP_001(const real_t *Jpt, real_t *P)

{

   real_t dI1[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).dI1(dI1));

   kernels::Set(2,2, 1.0, ie.Get_dI1(), P);

}


static MFEM_HOST_DEVICE inline

void EvalP_002(const real_t *Jpt, real_t *P)

{

   real_t dI1b[4], dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).dI1b(dI1b).dI2b(dI2b));

   kernels::Set(2,2, 0.5, ie.Get_dI1b(), P);

}


static MFEM_HOST_DEVICE inline

void EvalP_007(const real_t *Jpt, real_t *P)

{

   real_t dI1[4], dI2[4], dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).dI1(dI1)

                                     .dI2(dI2).dI2b(dI2b));

   const real_t I2 = ie.Get_I2();

   kernels::Add(2,2, 1.0 + 1.0 / I2, ie.Get_dI1(),

                -ie.Get_I1() / (I2*I2), ie.Get_dI2(), P);

}


// P_56 = 0.5*(1 - 1/I2b^2)*dI2b.

static MFEM_HOST_DEVICE inline

void EvalP_056(const real_t *Jpt, real_t *P)

{

   real_t dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).dI2b(dI2b));

   const real_t I2b = ie.Get_I2b();

   kernels::Set(2,2, 0.5 * (1.0 - 1.0 / (I2b * I2b)), ie.Get_dI2b(), P);

}


static MFEM_HOST_DEVICE inline

void EvalP_077(const real_t *Jpt, real_t *P)

{

   real_t dI2[4], dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().

                                     J(Jpt).

                                     dI2(dI2).dI2b(dI2b));

   const real_t I2 = ie.Get_I2();

   kernels::Set(2,2, 0.5 * (1.0 - 1.0 / (I2 * I2)), ie.Get_dI2(), P);

}


// P_80 = w0 P_2 + w1 P_77.

static MFEM_HOST_DEVICE inline

void EvalP_080(const real_t *Jpt, const real_t *w, real_t *P)

{

   real_t dI1b[4], dI2[4], dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).

                                     dI1b(dI1b).dI2(dI2).dI2b(dI2b));


   kernels::Set(2,2, w[0] * 0.5, ie.Get_dI1b(), P);


   const real_t I2 = ie.Get_I2();

   kernels::Add(2,2, w[1] * 0.5 * (1.0 - 1.0 / (I2 * I2)), ie.Get_dI2(), P);

}


// P_94 = w0 P_2 + w1 P_56.

static MFEM_HOST_DEVICE inline

void EvalP_094(const real_t *Jpt, const real_t *w, real_t *P)

{

   real_t dI1b[4], dI2b[4];

   kernels::InvariantsEvaluator2D ie(Args().J(Jpt).

                                     dI1b(dI1b).dI2b(dI2b));


   kernels::Set(2,2, w[0] * 0.5, ie.Get_dI1b(), P);


   const real_t I2b = ie.Get_I2b();

   kernels::Add(2,2, w[1] * 0.5 * (1.0 - 1.0 / (I2b * I2b)), ie.Get_dI2b(), P);

}


MFEM_REGISTER_TMOP_KERNELS(void, AddMultPA_Kernel_2D,

                           const real_t metric_normal,

                           const Vector &mc_,

                           const Array<real_t> &metric_param,

                           const int mid,

                           const int NE,

                           const DenseTensor &j_,

                           const Array<real_t> &w_,

                           const Array<real_t> &b_,

                           const Array<real_t> &g_,

                           const Vector &x_,

                           Vector &y_,

                           const int d1d,

                           const int q1d)

{

   MFEM_VERIFY(mid == 1 || mid == 2 || mid == 7 || mid == 77

               || mid == 80 || mid == 94,

               "2D metric not yet implemented!");


   const bool const_m0 = mc_.Size() == 1;


   constexpr int DIM = 2;

   constexpr int NBZ = 1;


   const int D1D = T_D1D ? T_D1D : d1d;

   const int Q1D = T_Q1D ? T_Q1D : q1d;


   const auto MC = const_m0 ?

                   Reshape(mc_.Read(), 1, 1, 1) :

                   Reshape(mc_.Read(), Q1D, Q1D, NE);

   const auto J = Reshape(j_.Read(), DIM, DIM, Q1D, Q1D, NE);

   const auto W = Reshape(w_.Read(), Q1D, Q1D);

   const auto b = Reshape(b_.Read(), Q1D, D1D);

   const auto g = Reshape(g_.Read(), Q1D, D1D);

   auto X = Reshape(x_.Read(), D1D, D1D, DIM, NE);

   auto Y = Reshape(y_.ReadWrite(), D1D, D1D, DIM, NE);


   const real_t *metric_data = metric_param.Read();


   mfem::forall_2D_batch(NE, Q1D, Q1D, NBZ, [=] MFEM_HOST_DEVICE (int e)

   {

      constexpr int NBZ = 1;

      constexpr int MQ1 = T_Q1D ? T_Q1D : T_MAX;

      constexpr int MD1 = T_D1D ? T_D1D : T_MAX;

      const int D1D = T_D1D ? T_D1D : d1d;

      const int Q1D = T_Q1D ? T_Q1D : q1d;


      MFEM_SHARED real_t BG[2][MQ1*MD1];

      MFEM_SHARED real_t XY[2][NBZ][MD1*MD1];

      MFEM_SHARED real_t DQ[4][NBZ][MD1*MQ1];

      MFEM_SHARED real_t QQ[4][NBZ][MQ1*MQ1];


      kernels::internal::LoadX<MD1,NBZ>(e,D1D,X,XY);

      kernels::internal::LoadBG<MD1,MQ1>(D1D,Q1D,b,g,BG);


      kernels::internal::GradX<MD1,MQ1,NBZ>(D1D,Q1D,BG,XY,DQ);

      kernels::internal::GradY<MD1,MQ1,NBZ>(D1D,Q1D,BG,DQ,QQ);


      MFEM_FOREACH_THREAD(qy,y,Q1D)

      {

         MFEM_FOREACH_THREAD(qx,x,Q1D)

         {

            const real_t *Jtr = &J(0,0,qx,qy,e);

            const real_t detJtr = kernels::Det<2>(Jtr);

            const real_t m_coef = const_m0 ? MC(0,0,0) : MC(qx,qy,e);

            const real_t weight = metric_normal * m_coef *

                                  W(qx,qy) * detJtr;


            // Jrt = Jtr^{-1}

            real_t Jrt[4];

            kernels::CalcInverse<2>(Jtr, Jrt);


            // Jpr = X{^T}.DSh

            real_t Jpr[4];

            kernels::internal::PullGrad<MQ1,NBZ>(Q1D,qx,qy,QQ,Jpr);


            // Jpt = X{^T}.DS = (X{^T}.DSh).Jrt = Jpr.Jrt

            real_t Jpt[4];

            kernels::Mult(2,2,2, Jpr, Jrt, Jpt);


            // metric->EvalP(Jpt, P);

            real_t P[4];

            if (mid ==  1) { EvalP_001(Jpt, P); }

            if (mid ==  2) { EvalP_002(Jpt, P); }

            if (mid ==  7) { EvalP_007(Jpt, P); }

            if (mid == 56) { EvalP_056(Jpt, P); }

            if (mid == 77) { EvalP_077(Jpt, P); }

            if (mid == 80) { EvalP_080(Jpt, metric_data, P); }

            if (mid == 94) { EvalP_094(Jpt, metric_data, P); }

            for (int i = 0; i < 4; i++) { P[i] *= weight; }


            // PMatO += DS . P^t += DSh . (Jrt . P^t)

            real_t A[4];

            kernels::MultABt(2,2,2, Jrt, P, A);

            kernels::internal::PushGrad<MQ1,NBZ>(Q1D,qx,qy,A,QQ);

         }

      }

      MFEM_SYNC_THREAD;

      kernels::internal::LoadBGt<MD1,MQ1>(D1D,Q1D,b,g,BG);

      kernels::internal::GradYt<MD1,MQ1,NBZ>(D1D,Q1D,BG,QQ,DQ);

      kernels::internal::GradXt<MD1,MQ1,NBZ>(D1D,Q1D,BG,DQ,Y,e);

   });

}


void TMOP_Integrator::AddMultPA_2D(const Vector &X, Vector &Y) const

{

   const int N = PA.ne;

   const int M = metric->Id();

   const int D1D = PA.maps->ndof;

   const int Q1D = PA.maps->nqpt;

   const int id = (D1D << 4 ) | Q1D;

   const DenseTensor &J = PA.Jtr;

   const Array<real_t> &W = PA.ir->GetWeights();

   const Array<real_t> &B = PA.maps->B;

   const Array<real_t> &G = PA.maps->G;

   const real_t mn = metric_normal;

   const Vector &MC = PA.MC;


   Array<real_t> mp;

   if (auto m = dynamic_cast<TMOP_Combo_QualityMetric *>(metric))

   {

      m->GetWeights(mp);

   }


   MFEM_LAUNCH_TMOP_KERNEL(AddMultPA_Kernel_2D,id,mn,MC,mp,M,N,J,W,B,G,X,Y);

}


} // namespace mfem

mfem::Array
Definition array.hpp:47

mfem::Array::Read
const T * Read(bool on_dev=true) const
Shortcut for mfem::Read(a.GetMemory(), a.Size(), on_dev).
Definition array.hpp:337

mfem::DenseTensor
Rank 3 tensor (array of matrices)
Definition densemat.hpp:1112

mfem::DenseTensor::Read
const real_t * Read(bool on_dev=true) const
Shortcut for mfem::Read( GetMemory(), TotalSize(), on_dev).
Definition densemat.hpp:1270

mfem::TMOP_Combo_QualityMetric
Definition tmop.hpp:96

mfem::TMOP_Integrator::metric
TMOP_QualityMetric * metric
Definition tmop.hpp:1899

mfem::TMOP_Integrator::AddMultPA_2D
void AddMultPA_2D(const Vector &, Vector &) const
Definition tmop_pa_p2.cpp:203

mfem::TMOP_Integrator::metric_normal
real_t metric_normal
Definition tmop.hpp:1910

mfem::TMOP_Integrator::PA
struct mfem::TMOP_Integrator::@26 PA

mfem::TMOP_Integrator::MC
Vector MC
Definition tmop.hpp:2019

mfem::TMOP_QualityMetric::Id
virtual int Id() const
Return the metric ID.
Definition tmop.hpp:88

mfem::Vector
Vector data type.
Definition vector.hpp:82

mfem::Vector::Read
virtual const real_t * Read(bool on_dev=true) const
Shortcut for mfem::Read(vec.GetMemory(), vec.Size(), on_dev).
Definition vector.hpp:494

mfem::Vector::ReadWrite
virtual real_t * ReadWrite(bool on_dev=true)
Shortcut for mfem::ReadWrite(vec.GetMemory(), vec.Size(), on_dev).
Definition vector.hpp:510

mfem::Vector::Size
int Size() const
Returns the size of the vector.
Definition vector.hpp:226

dinvariants.hpp

forall.hpp

kernels.hpp

b
real_t b
Definition lissajous.cpp:42

DIM
constexpr int DIM
Definition minimal-surface.cpp:72

mfem::kernels::CalcInverse
MFEM_HOST_DEVICE void CalcInverse(const T *data, T *inv_data)
Return the inverse of a matrix with given size and data into the matrix with data inv_data.
Definition kernels.hpp:246

mfem::kernels::Add
MFEM_HOST_DEVICE void Add(const int height, const int width, const TALPHA alpha, const TA *Adata, const TB *Bdata, TC *Cdata)
Compute C = A + alpha*B, where the matrices A, B and C are of size height x width with data Adata,...
Definition kernels.hpp:266

mfem::kernels::Mult
MFEM_HOST_DEVICE void Mult(const int height, const int width, const TA *data, const TX *x, TY *y)
Matrix vector multiplication: y = A x, where the matrix A is of size height x width with given data,...
Definition kernels.hpp:163

mfem::kernels::MultABt
MFEM_HOST_DEVICE void MultABt(const int Aheight, const int Awidth, const int Bheight, const TA *Adata, const TB *Bdata, TC *ABtdata)
Multiply a matrix of size Aheight x Awidth and data Adata with the transpose of a matrix of size Bhei...
Definition kernels.hpp:383

mfem::kernels::Set
MFEM_HOST_DEVICE void Set(const int height, const int width, const real_t alpha, const TA *Adata, TB *Bdata)
Compute B = alpha*A, where the matrices A and B are of size height x width with data Adata and Bdata.
Definition kernels.hpp:326

mfem::kernels::Det
MFEM_HOST_DEVICE T Det(const T *data)
Compute the determinant of a square matrix of size dim with given data.
Definition kernels.hpp:237

mfem
Definition CodeDocumentation.dox:1

mfem::MFEM_REGISTER_TMOP_KERNELS
MFEM_REGISTER_TMOP_KERNELS(void, DatcSize, const int NE, const int ncomp, const int sizeidx, const real_t input_min_size, const DenseMatrix &w_, const Array< real_t > &b_, const Vector &x_, const Vector &nc_reduce, DenseTensor &j_, const int d1d, const int q1d)
Definition tmop_pa_da3.cpp:20

mfem::Reshape
MFEM_HOST_DEVICE DeviceTensor< sizeof...(Dims), T > Reshape(T *ptr, Dims... dims)
Wrap a pointer as a DeviceTensor with automatically deduced template parameters.
Definition dtensor.hpp:131

mfem::forall_2D_batch
void forall_2D_batch(int N, int X, int Y, int BZ, lambda &&body)
Definition forall.hpp:768

mfem::Args
kernels::InvariantsEvaluator2D::Buffers Args
Definition tmop_pa_h2s.cpp:21

mfem::real_t
float real_t
Definition config.hpp:43

tmop.hpp

tmop_pa.hpp