def test_laplace():
laplace = lambda u, x, y: diff(u, x, order=2) + diff(u, y, order=2)
bc = DirichletBVP2D(x_min=0,
x_min_val=lambda y: torch.sin(np.pi * y),
x_max=1,
x_max_val=lambda y: 0,
y_min=0,
y_min_val=lambda x: 0,
y_max=1,
y_max_val=lambda x: 0)
net = FCNN(n_input_units=2, hidden_units=(32, 32))
solution_neural_net_laplace, _ = solve2D(pde=laplace,
condition=bc,
xy_min=(0, 0),
xy_max=(1, 1),
net=net,
max_epochs=300,
train_generator=Generator2D(
(32, 32), (0, 0), (1, 1),
method='equally-spaced-noisy',
xy_noise_std=(0.01, 0.01)),
batch_size=64)
solution_analytical_laplace = lambda x, y: np.sin(np.pi * y) * np.sinh(
np.pi * (1 - x)) / np.sinh(np.pi)
xs, ys = np.linspace(0, 1, 101), np.linspace(0, 1, 101)
xx, yy = np.meshgrid(xs, ys)
sol_net = solution_neural_net_laplace(xx, yy, as_type='np')
sol_ana = solution_analytical_laplace(xx, yy)
assert isclose(sol_net, sol_ana, atol=0.01).all()
print('Laplace test passed.')
def test_monitor():
laplace = lambda u, x, y: diff(u, x, order=2) + diff(u, y, order=2)
bc = DirichletBVP2D(x_min=0,
x_min_val=lambda y: torch.sin(np.pi * y),
x_max=1,
x_max_val=lambda y: 0,
y_min=0,
y_min_val=lambda x: 0,
y_max=1,
y_max_val=lambda x: 0)
net = FCNN(n_input_units=2, hidden_units=(32, 32))
solution_neural_net_laplace, _ = solve2D(
pde=laplace,
condition=bc,
xy_min=(0, 0),
xy_max=(1, 1),
net=net,
max_epochs=3,
train_generator=Generator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced-noisy'),
batch_size=64,
monitor=Monitor2D(check_every=1, xy_min=(0, 0), xy_max=(1, 1)))
print('Monitor test passed.')
def test_train_generator():
laplace = lambda u, x, y: diff(u, x, order=2) + diff(u, y, order=2)
bc = DirichletBVP2D(x_min=0,
x_min_val=lambda y: torch.sin(np.pi * y),
x_max=1,
x_max_val=lambda y: 0,
y_min=0,
y_min_val=lambda x: 0,
y_max=1,
y_max_val=lambda x: 0)
net = FCNN(n_input_units=2, n_hidden_units=32, n_hidden_layers=1)
solution_neural_net_laplace, _ = solve2D(
pde=laplace,
condition=bc,
xy_min=(0, 0),
xy_max=(1, 1),
net=net,
max_epochs=3,
train_generator=ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced-noisy'),
batch_size=64,
monitor=Monitor2D(check_every=1, xy_min=(0, 0), xy_max=(1, 1)))
train_gen = ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced')
solution_neural_net_laplace, _ = solve2D(pde=laplace,
condition=bc,
xy_min=(0, 0),
xy_max=(1, 1),
net=net,
max_epochs=3,
train_generator=train_gen,
batch_size=64)
train_gen = ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced-noisy')
solution_neural_net_laplace, _ = solve2D(pde=laplace,
condition=bc,
xy_min=(0, 0),
xy_max=(1, 1),
net=net,
max_epochs=3,
train_generator=train_gen,
batch_size=64)
with raises(ValueError):
train_gen = ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='magic')
print('ExampleGenerator test passed.')
valid_gen = ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced-noisy')
train_gen = ExampleGenerator2D((32, 32), (0, 0), (1, 1),
method='equally-spaced')
solution_neural_net_laplace, _ = solve2D(pde=laplace,
condition=bc,
net=net,
max_epochs=3,
train_generator=train_gen,
valid_generator=valid_gen,
batch_size=64)
with raises(RuntimeError):
solution_neural_net_laplace, _ = solve2D(pde=laplace,
condition=bc,
net=net,
max_epochs=3,
batch_size=64)