df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
from pina.equation import Equation
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from pina.operator import grad, laplacian
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from pina import LabelTensor
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import torch
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import pytest
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def eq1(input_, output_):
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u_grad = grad(output_, input_)
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u1_xx = grad(u_grad, input_, components=['du1dx'], d=['x'])
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u2_xy = grad(u_grad, input_, components=['du2dx'], d=['y'])
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return torch.hstack([u1_xx, u2_xy])
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def eq2(input_, output_):
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force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
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torch.sin(input_.extract(['y']) * torch.pi))
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delta_u = laplacian(output_.extract(['u1']), input_)
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return delta_u - force_term
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def foo():
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pass
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def test_constructor():
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Equation(eq1)
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Equation(eq2)
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with pytest.raises(ValueError):
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Equation([1, 2, 4])
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with pytest.raises(ValueError):
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Equation(foo())
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def test_residual():
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eq_1 = Equation(eq1)
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eq_2 = Equation(eq2)
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pts = LabelTensor(torch.rand(10, 2), labels=['x', 'y'])
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pts.requires_grad = True
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u = torch.pow(pts, 2)
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u.labels = ['u1', 'u2']
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eq_1_res = eq_1.residual(pts, u)
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eq_2_res = eq_2.residual(pts, u)
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assert eq_1_res.shape == torch.Size([10, 2])
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assert eq_2_res.shape == torch.Size([10, 1])
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