Backend paddle: Support elasticity_plate example

examples/pinn_forward/elasticity_plate.py

@@ -1,17 +1,27 @@
-"""Backend supported: pytorch
+"""Backend supported: pytorch, paddle
 
 Implementation of the linear elasticity 2D example in paper https://doi.org/10.1016/j.cma.2021.113741.
 References:
  https://github.com/sciann/sciann-applications/blob/master/SciANN-Elasticity/Elasticity-Forward.ipynb.
 """
 import deepxde as dde
 import numpy as np
-import torch
 
 lmbd = 1.0
 mu = 0.5
 Q = 4.0
-pi = torch.pi
+
+# Define function
+if dde.backend.backend_name == "pytorch":
+ import torch
+
+ sin = torch.sin
+ cos = torch.cos
+elif dde.backend.backend_name == "paddle":
+ import paddle
+
+ sin = paddle.sin
+ cos = paddle.cos
 
 geom = dde.geometry.Rectangle([0, 0], [1, 1])
 
@@ -35,10 +45,10 @@ def boundary_bottom(x, on_boundary):
 # Exact solutions
 def func(x):
  ux = np.cos(2 * np.pi * x[:, 0:1]) * np.sin(np.pi * x[:, 1:2])
- uy = np.sin(pi * x[:, 0:1]) * Q * x[:, 1:2] ** 4 / 4
+ uy = np.sin(np.pi * x[:, 0:1]) * Q * x[:, 1:2] ** 4 / 4
 
  E_xx = -2 * np.pi * np.sin(2 * np.pi * x[:, 0:1]) * np.sin(np.pi * x[:, 1:2])
- E_yy = np.sin(pi * x[:, 0:1]) * Q * x[:, 1:2] ** 3
+ E_yy = np.sin(np.pi * x[:, 0:1]) * Q * x[:, 1:2] ** 3
  E_xy = 0.5 * (
  np.pi * np.cos(2 * np.pi * x[:, 0:1]) * np.cos(np.pi * x[:, 1:2])
  + np.pi * np.cos(np.pi * x[:, 0:1]) * Q * x[:, 1:2] ** 4 / 4
@@ -60,7 +70,7 @@ def func(x):
 sxx_right_bc = dde.icbc.DirichletBC(geom, lambda x: 0, boundary_right, component=2)
 syy_top_bc = dde.icbc.DirichletBC(
  geom,
- lambda x: (2 * mu + lmbd) * Q * np.sin(pi * x[:, 0:1]),
+ lambda x: (2 * mu + lmbd) * Q * np.sin(np.pi * x[:, 0:1]),
  boundary_top,
  component=3,
 )
@@ -70,31 +80,31 @@ def fx(x):
  return (
  -lmbd
  * (
- 4 * pi**2 * torch.cos(2 * pi * x[:, 0:1]) * torch.sin(pi * x[:, 1:2])
- - Q * x[:, 1:2] ** 3 * pi * torch.cos(pi * x[:, 0:1])
+ 4 * np.pi**2 * cos(2 * np.pi * x[:, 0:1]) * sin(np.pi * x[:, 1:2])
+ - Q * x[:, 1:2] ** 3 * np.pi * cos(np.pi * x[:, 0:1])
  )
  - mu
  * (
- pi**2 * torch.cos(2 * pi * x[:, 0:1]) * torch.sin(pi * x[:, 1:2])
- - Q * x[:, 1:2] ** 3 * pi * torch.cos(pi * x[:, 0:1])
+ np.pi**2 * cos(2 * np.pi * x[:, 0:1]) * sin(np.pi * x[:, 1:2])
+ - Q * x[:, 1:2] ** 3 * np.pi * cos(np.pi * x[:, 0:1])
  )
- - 8 * mu * pi**2 * torch.cos(2 * pi * x[:, 0:1]) * torch.sin(pi * x[:, 1:2])
+ - 8 * mu * np.pi**2 * cos(2 * np.pi * x[:, 0:1]) * sin(np.pi * x[:, 1:2])
  )
 
 
 def fy(x):
  return (
  lmbd
  * (
- 3 * Q * x[:, 1:2] ** 2 * torch.sin(pi * x[:, 0:1])
- - 2 * pi**2 * torch.cos(pi * x[:, 1:2]) * torch.sin(2 * pi * x[:, 0:1])
+ 3 * Q * x[:, 1:2] ** 2 * sin(np.pi * x[:, 0:1])
+ - 2 * np.pi**2 * cos(np.pi * x[:, 1:2]) * sin(2 * np.pi * x[:, 0:1])
  )
  - mu
  * (
- 2 * pi**2 * torch.cos(pi * x[:, 1:2]) * torch.sin(2 * pi * x[:, 0:1])
- + (Q * x[:, 1:2] ** 4 * pi**2 * torch.sin(pi * x[:, 0:1])) / 4
+ 2 * np.pi**2 * cos(np.pi * x[:, 1:2]) * sin(2 * np.pi * x[:, 0:1])
+ + (Q * x[:, 1:2] ** 4 * np.pi**2 * sin(np.pi * x[:, 0:1])) / 4
  )
- + 6 * Q * mu * x[:, 1:2] ** 2 * torch.sin(pi * x[:, 0:1])
+ + 6 * Q * mu * x[:, 1:2] ** 2 * sin(np.pi * x[:, 0:1])
  )