def test_functional_exceptions(variable_x):
x = variable_x
with pytest.raises(TypeError):
f = sn.Functional(x)
with pytest.raises(TypeError):
ft = sn.Functional('ft', 2 * [10])
with pytest.raises(TypeError):
ft = sn.Functional('ft', x, 'tanh')
with pytest.raises(TypeError):
ft = sn.Functional('ft', x, 2 * [10], 12)
def functional_hxy(variable_x, variable_y):
return sn.Functional('hxy', [variable_x, variable_y],
2 * [10],
'tanh',
kernel_initializer=1.,
bias_initializer=0.)
def functional_gxy(variable_x, variable_y):
return sn.Functional('gxy', [variable_x, variable_y], 2 * [10], 'tanh')
def functional_gx(variable_x):
return sn.Functional('gx', variable_x, 2 * [10], 'tanh')
def test_activation_function_exceptions(variable_x):
x = variable_x
assert sn.is_functional(sn.Functional('ft', x, 2 * [10], 'l-tanh'))
assert sn.is_functional(sn.Functional('gt', x, 2 * [10], 'g-tanh'))
assert sn.is_functional(
sn.Functional('rt', x, 2 * [10], ['tanh', 'sin', 'l-tanh']))
def test_functional(variable_x):
x = variable_x
ft = sn.Functional('ft', x, 2 * [10], 'tanh')
assert sn.is_functional(ft)
T_Final = 0.1
NX = 20
NY = 20
NT = 40
NTOT = NX * NY * NT
EPOCHS = 20000
BATCH = 1000
data = gen_grid(NX, NY, NT, Lx, Ly, T_Final)
x = sn.Variable('x', dtype='float64')
y = sn.Variable('y', dtype='float64')
t = sn.Variable('t', dtype='float64')
u = sn.Functional('u', [x, y, t], 4 * [40], 'l-tanh')
L1 = D * (diff(u, x, order=4) + diff(u, y, order=4) + 2 *
diff(diff(u, x, order=2), y, order=2)) + rho * diff(u, t, order=2)
TOL = 0.001
C1 = (1 - sign(t - TOL)) * (u - sin(np.pi * x) * sin(np.pi * y))
C2 = (1 - sign(t - TOL)) * (diff(u, t))
C3 = (1 - sign(x - TOL)) * u
C4 = (1 - sign(y - TOL)) * u
C5 = (1 + sign(x - (1 - TOL))) * u
C6 = (1 + sign(y - (1 - TOL))) * u
C7 = (1 - sign(x - TOL)) * (diff(u, x, order=2))
C8 = (1 - sign(y - TOL)) * (diff(u, y, order=2))
T_Final = 0.5
NX = 40
NY = 40
NT = 20
EPOCHS = 2000
BATCH = 1000
data = gen_grid(NX, NY, NT, Lx, Ly, T_Final)
# x_data, y_data, t_data = np.meshgrid(np.linspace(0, Lx, NX), np.linspace(0, Ly, NY), np.linspace(0, T_Final, NT))
x = sn.Variable('x', dtype='float64')
y = sn.Variable('y', dtype='float64')
t = sn.Variable('t', dtype='float64')
u = sn.Functional('u', [x, y, t], 4 * [20], 'sin')
c = 1.0
L1 = c * (diff(u, x, order=2) + diff(u, y, order=2)) - diff(u, t, order=2)
TOL = 0.001
C1 = (1 - sign(t - TOL)) * (u - sin(np.pi * x) * sin(np.pi * y))
C2 = (1 - sign(t - TOL)) * (diff(u, t))
C3 = (1 - sign(x - TOL)) * u
C4 = (1 - sign(y - TOL)) * u
C5 = (1 + sign(x - (1 - TOL))) * u
C6 = (1 + sign(y - (1 - TOL))) * u
m = sn.SciModel(
[x, y, t],
[sn.PDE(L1), C1, C2, C3, C4, C5, C6],
from sciann.utils.math import diff
import sciann as sn
sn.set_random_seed(1234)
# Synthetic data generated from sin function over [0, 2pi]
x_true = np.linspace(0, np.pi*2, 10000)
y_true = np.sin(x_true)
# The network inputs should be defined with Variable.
x = Variable('x', dtype='float64')
xf = sn.fourier(x, 10)
# Each network is defined by Functional.
y1 = sn.Field('y1', 10)
y2 = sn.Field('y2', 10)
y1, y2 = sn.Functional([y1,y2], xf, [10, 10, 10], 'l-tanh', output_activation='tanh')
y = sn.Functional('y', [xf*y1, xf*y2])
d = Parameter(10.0, inputs=x, name='d')
# Define the target (output) of your model.
c1 = Data(y)
L = d*diff(y, x, order=2) + y
# The model is formed with input `x` and condition `c1`.
model = SciModel(x, [c1, sn.PDE(L)])
# Tra: .train runs the optimization and finds the parameters.
history = model.train(