50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
from pina.equation import Equation
|
|
from pina.operator import grad, laplacian
|
|
from pina import LabelTensor
|
|
import torch
|
|
import pytest
|
|
|
|
|
|
def eq1(input_, output_):
|
|
u_grad = grad(output_, input_)
|
|
u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"])
|
|
u2_xy = grad(u_grad, input_, components=["du2dx"], d=["y"])
|
|
return torch.hstack([u1_xx, u2_xy])
|
|
|
|
|
|
def eq2(input_, output_):
|
|
force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin(
|
|
input_.extract(["y"]) * torch.pi
|
|
)
|
|
delta_u = laplacian(output_.extract(["u1"]), input_)
|
|
return delta_u - force_term
|
|
|
|
|
|
def foo():
|
|
pass
|
|
|
|
|
|
def test_constructor():
|
|
Equation(eq1)
|
|
Equation(eq2)
|
|
with pytest.raises(ValueError):
|
|
Equation([1, 2, 4])
|
|
with pytest.raises(ValueError):
|
|
Equation(foo())
|
|
|
|
|
|
def test_residual():
|
|
eq_1 = Equation(eq1)
|
|
eq_2 = Equation(eq2)
|
|
|
|
pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"])
|
|
pts.requires_grad = True
|
|
u = torch.pow(pts, 2)
|
|
u.labels = ["u1", "u2"]
|
|
|
|
eq_1_res = eq_1.residual(pts, u)
|
|
eq_2_res = eq_2.residual(pts, u)
|
|
|
|
assert eq_1_res.shape == torch.Size([10, 2])
|
|
assert eq_2_res.shape == torch.Size([10, 1])
|