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def boundary_r(x, on_boundary):
return on_boundary and np.isclose(x[0], 1)
def func(x):
return (x + 1) ** 2
geom = dde.geometry.Interval(-1, 1)
bc_l = dde.DirichletBC(geom, func, boundary_l)
bc_r = dde.RobinBC(geom, lambda X, y: y, boundary_r)
data = dde.data.PDE(geom, 1, pde, [bc_l, bc_r], 16, 2, func=func, num_test=100)
layer_size = [1] + [50] * 3 + [1]
activation = "tanh"
initializer = "Glorot uniform"
net = dde.maps.FNN(layer_size, activation, initializer)
model = dde.Model(data, net)
model.compile("adam", lr=0.001, metrics=["l2 relative error"])
losshistory, train_state = model.train(epochs=10000)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
x: array_like, N x D_in
y: array_like, N x D_out
"""
return x * np.sin(5 * x)
geom = dde.geometry.Interval(-1, 1)
num_train = 10
num_test = 1000
data = dde.data.Func(geom, func, num_train, num_test)
layer_size = [1] + [50] * 3 + [1]
activation = "tanh"
initializer = "Glorot uniform"
regularization = ["l2", 1e-5]
dropout_rate = 0.01
net = dde.maps.FNN(
layer_size,
activation,
initializer,
regularization=regularization,
dropout_rate=dropout_rate,
)
model = dde.Model(data, net)
model.compile("adam", lr=0.001, metrics=["l2 relative error"])
losshistory, train_state = model.train(epochs=30000, uncertainty=True)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
def boundary_r(x, on_boundary):
return on_boundary and np.isclose(x[0], 1)
def func(x):
return (x + 1) ** 2
geom = dde.geometry.Interval(-1, 1)
bc_l = dde.DirichletBC(geom, func, boundary_l)
bc_r = dde.NeumannBC(geom, lambda X: 2 * (X + 1), boundary_r)
data = dde.data.PDE(geom, 1, pde, [bc_l, bc_r], 16, 2, func=func, num_test=100)
layer_size = [1] + [50] * 3 + [1]
activation = "tanh"
initializer = "Glorot uniform"
net = dde.maps.FNN(layer_size, activation, initializer)
model = dde.Model(data, net)
model.compile("adam", lr=0.001, metrics=["l2 relative error"])
losshistory, train_state = model.train(epochs=10000)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
def func(x):
"""
y1 = sin(x)
y2 = cos(x)
"""
return np.hstack((np.sin(x), np.cos(x)))
geom = dde.geometry.Interval(0, 10)
bc1 = dde.DirichletBC(geom, np.sin, boundary, component=0)
bc2 = dde.DirichletBC(geom, np.cos, boundary, component=1)
data = dde.data.PDE(geom, 2, ode_system, [bc1, bc2], 35, 2, func=func, num_test=100)
layer_size = [1] + [50] * 3 + [2]
activation = "tanh"
initializer = "Glorot uniform"
net = dde.maps.FNN(layer_size, activation, initializer)
model = dde.Model(data, net)
model.compile("adam", lr=0.001, metrics=["l2 relative error"])
losshistory, train_state = model.train(epochs=20000)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)