def main():
geom = dde.geometry.Disk([0, 0], 1)
observe_x = geom.random_points(30)
observe_y = dde.PointSetBC(observe_x, func(observe_x))
data = dde.data.FPDE(
geom,
fpde,
alpha,
observe_y,
[8, 100],
num_domain=64,
anchors=observe_x,
solution=func,
)
net = dde.maps.FNN([2] + [20] * 4 + [1], "tanh", "Glorot normal")
net.apply_output_transform(
lambda x, y: (1 - tf.reduce_sum(x**2, axis=1, keepdims=True)) * y)
model = dde.Model(data, net)
model.compile("adam", lr=1e-3, loss_weights=[1, 100])
variable = dde.callbacks.VariableValue(alpha, period=1000)
losshistory, train_state = model.train(epochs=10000, callbacks=[variable])
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
def main():
geom = dde.geometry.Sphere([0, 0, 0], 1)
bc = dde.DirichletBC(geom, func, lambda _, on_boundary: on_boundary)
data = dde.data.FPDE(
geom,
fpde,
alpha,
bc,
[8, 8, 100],
num_domain=256,
num_boundary=1,
solution=func,
)
net = dde.maps.FNN([3] + [20] * 4 + [1], "tanh", "Glorot normal")
net.apply_output_transform(
lambda x, y: (1 - tf.reduce_sum(x**2, axis=1, keepdims=True)) * y)
model = dde.Model(data, net)
model.compile("adam", lr=1e-3)
losshistory, train_state = model.train(epochs=10000)
dde.saveplot(losshistory, train_state, issave=False, isplot=True)
X = geom.random_points(10000)
y_true = func(X)
y_pred = model.predict(X)
print("L2 relative error:", dde.metrics.l2_relative_error(y_true, y_pred))
np.savetxt("test.dat", np.hstack((X, y_true, y_pred)))
def main():
alpha0 = 1.8
alpha = tf.Variable(1.5)
def fpde(x, y, int_mat):
"""\int_theta D_theta^alpha u(x)
"""
if isinstance(int_mat, (list, tuple)) and len(int_mat) == 3:
int_mat = tf.SparseTensor(*int_mat)
lhs = tf.sparse_tensor_dense_matmul(int_mat, y)
else:
lhs = tf.matmul(int_mat, y)
lhs = lhs[:, 0]
lhs *= -tf.exp(
tf.lgamma((1 - alpha) / 2) + tf.lgamma(
(2 + alpha) / 2)) / (2 * np.pi**1.5)
x = x[:tf.size(lhs)]
rhs = (2**alpha0 * gamma(2 + alpha0 / 2) * gamma(1 + alpha0 / 2) *
(1 - (1 + alpha0 / 2) * tf.reduce_sum(x**2, axis=1)))
return lhs - rhs
def func(x):
return (1 - np.linalg.norm(x, axis=1, keepdims=True)**2)**(1 +
alpha0 / 2)
geom = dde.geometry.Disk([0, 0], 1)
observe_x = geom.random_points(30)
ptset = dde.bc.PointSet(observe_x)
observe_y = dde.DirichletBC(geom, ptset.values_to_func(func(observe_x)),
lambda x, _: ptset.inside(x))
data = dde.data.FPDE(
geom,
fpde,
alpha,
observe_y,
[8, 100],
num_domain=64,
anchors=observe_x,
solution=func,
)
net = dde.maps.FNN([2] + [20] * 4 + [1], "tanh", "Glorot normal")
net.apply_output_transform(
lambda x, y: (1 - tf.reduce_sum(x**2, axis=1, keepdims=True)) * y)
model = dde.Model(data, net)
model.compile("adam", lr=1e-3, loss_weights=[1, 100])
variable = dde.callbacks.VariableValue(alpha, period=1000)
losshistory, train_state = model.train(epochs=10000, callbacks=[variable])
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
def fpde(x, y, int_mat):
"""\int_theta D_theta^alpha u(x)"""
if isinstance(int_mat, (list, tuple)) and len(int_mat) == 3:
int_mat = tf.SparseTensor(*int_mat)
lhs = tf.sparse_tensor_dense_matmul(int_mat, y)
else:
lhs = tf.matmul(int_mat, y)
lhs = lhs[:, 0]
lhs *= gamma((1 - alpha) / 2) * gamma((3 + alpha) / 2) / (2 * np.pi**2)
x = x[:tf.size(lhs)]
rhs = (2**alpha * gamma(2 + alpha / 2) * gamma((3 + alpha) / 2) /
gamma(3 / 2) * (1 - (1 + alpha / 3) * tf.reduce_sum(x**2, axis=1)))
return lhs - rhs
def main():
alpha = 1.8
def fpde(x, y, int_mat):
"""\int_theta D_theta^alpha u(x)
"""
if isinstance(int_mat, (list, tuple)) and len(int_mat) == 3:
int_mat = tf.SparseTensor(*int_mat)
lhs = tf.sparse_tensor_dense_matmul(int_mat, y)
else:
lhs = tf.matmul(int_mat, y)
lhs = lhs[:, 0]
lhs *= gamma((1 - alpha) / 2) * gamma(
(2 + alpha) / 2) / (2 * np.pi**1.5)
x = x[:tf.size(lhs)]
rhs = (2**alpha * gamma(2 + alpha / 2) * gamma(1 + alpha / 2) *
(1 - (1 + alpha / 2) * tf.reduce_sum(x**2, axis=1)))
return lhs - rhs
def func(x):
return (np.abs(1 - np.linalg.norm(x, axis=1, keepdims=True)**2))**(
1 + alpha / 2)
geom = dde.geometry.Disk([0, 0], 1)
bc = dde.DirichletBC(geom, func, lambda _, on_boundary: on_boundary)
data = dde.data.FPDE(geom,
fpde,
alpha,
bc, [8, 100],
num_domain=100,
num_boundary=1,
solution=func)
net = dde.maps.FNN([2] + [20] * 4 + [1], "tanh", "Glorot normal")
net.apply_output_transform(
lambda x, y: (1 - tf.reduce_sum(x**2, axis=1, keepdims=True)) * y)
model = dde.Model(data, net)
model.compile("adam", lr=1e-3)
losshistory, train_state = model.train(epochs=20000)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
X = geom.random_points(1000)
y_true = func(X)
y_pred = model.predict(X)
print("L2 relative error:", dde.metrics.l2_relative_error(y_true, y_pred))
np.savetxt("test.dat", np.hstack((X, y_true, y_pred)))
