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Commit a8576079 authored by Jan Habscheid's avatar Jan Habscheid
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GP + ANN working

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heat_eq_data.npz deleted 100644 → 0
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# step, loss_train, loss_test, metrics_test
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src/ANN.ipynb
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src/DEEPxde.py 0 → 100644
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import deepxde as dde
import numpy as np
import matplotlib.pyplot as plt
from FEniCSx_NonLinearHeatEquation import solve_heatequation
from HeatCoefficients import kappa_Klinger, kappa_Klinger_prime, kappa_Slack, kappa_Slack_prime
def heat_equation(x, T_degC):
'''
T is in deg_C
'''
T_K = T_degC + 273.15
kappa = kappa_Klinger(T_K)
kappa_prime = kappa_Klinger_prime(T_K)
# Gradient is the same for T_degC and T_K
dT_x = dde.grad.jacobian(T_degC, x, i=0, j=0)
dT_xx = dde.grad.hessian(T_degC, x, i=0, j=0)
return kappa_prime * dT_x + kappa * dT_xx # dt_x**2 or dT_x?
# return kappa * dT_xx
# print(f'kappa: {kappa}')
# print(f'dT_x: {dT_x}')
# print(f'kappa * dT_x: {kappa * dT_x}')
# # print(f'dde.grad.jacobian(kappa * dT_x): {dde.grad.jacobian(kappa * dT_x)}')
# print(f'dde.grad.gradients(kappa * dT_x): {dde.grad.gradients(kappa * dT_x)}')
# rhs = dde.grad.jacobian(kappa * dT_x)
# return rhs
def numerical_solution(x):
# Fenics Temp is in Kelvin
return np.interp(x, x_fenics, T_fenics) - 273.15
def get_bcs(geom, T_left, T_right):
bc_L = dde.DirichletBC(geom, lambda x: T_left, lambda x, on_boundary: on_boundary and np.isclose(x[0], 0))
bc_R = dde.DirichletBC(geom, lambda x: T_right, lambda x, on_boundary: on_boundary and np.isclose(x[0], 1))
return [bc_L, bc_R]
def get_data(geom, pde, bcs, numerical_solution, num_domain=2540, num_boundary=80, num_test=100):
data = dde.data.PDE(
geom,
pde,
bcs,
num_domain=2540,
num_boundary=80,
num_test=100,
solution=numerical_solution,
)
return data
def get_numerical_solution(T_left, T_right, number_cells=1024):
T_fenics, x_fenics = solve_heatequation(T_left+273.15, T_right+273.15, kappa_Klinger, 1., number_cells=number_cells, return_type='Vector')
return T_fenics, x_fenics
def NN(neurons, activation='tanh', initializer='Glorot normal'):
return dde.nn.FNN(neurons, activation, initializer)
def train(net, data, iterations=10_000, batch_size=32, display_every=100, lr=0.001, optimizer='adam', callbacks=[]):
model = dde.Model(data, net)
model.compile(optimizer, lr=lr)
losshistory, train_state = model.train(iterations=iterations, display_every=display_every, batch_size=batch_size, callbacks=callbacks)
return losshistory, train_state
if __name__ == '__main__':
T_left_C = -200 # degC
T_right_C = 0 # degC
T_left_K = T_left_C + 273.15
T_right_K = T_right_C + 273.15
# Numerical Solution as reference
T_fenics, x_fenics = get_numerical_solution(T_left_K, T_right_K)
T_fenics = T_fenics - 273.15
activation = 'tanh'
initializer = 'Glorot normal'
neurons = [2] + [128] * 2 + [1] # change first to 1???
callbacks = [
dde.callbacks.EarlyStopping(
min_delta=1e-3,
patience=500,
monitor='loss_test', # loss_train or loss_test
start_from_epoch=1_000, # start checking after this epoch
),
dde.callbacks.MovieDumper(
filename='movie',
x1=0, # 5?
x2=10, # 5?
save_spectrum=True
),
dde.callbacks.PDEPointResampler(
period=100, # how often resample the points
pde_points=True, #resample training points for PDE loss
bc_points=False, # resmaple traaining points for BC loss (only supported by PyTorch and PaddlePaddle backend)
)
]
geom = dde.geometry.Interval(0, 1)
data = get_data(geom, heat_equation, get_bcs(geom, T_left_C, T_right_C), numerical_solution)
net = NN(neurons, activation, initializer)
losshistory, train_state = train(net, data, iterations=10_000, lr=0.001, optimizer='adam')
# Plot/print the results
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
test_data = np.loadtxt('test.dat')
x_test = test_data[:,0]
y_true = test_data[:,1]
y_pred = test_data[:,2]
plt.figure()
plt.plot(x_test, y_true, label='true')
plt.plot(x_test, y_pred, label='predict')
plt.plot([x_test[0], x_test[-1]], [y_pred[0], y_pred[-1]], '--', label='Linear')
plt.legend()
plt.show()
\ No newline at end of file
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......@@ -133,17 +133,16 @@ if __name__ == '__main__':
pinn = PINN(lb, ub, eps, T_left, T_right, kappa_type, N_COLLOCAtION_POINTS, INITIAL_LEARNING_RATE, REDUCE_LEARNING_RATE_FACTOR, REDUCE_LEARNIG_RATE_PATIENCE, REDUCE_LEARNING_RATE_INITIAL_PATIENCE, N_OPTIMIZATION_EPOCHS, BC_LOSS_WEIGHT, pde_loss_contribution_factor)
pinn.create_PINN_default(42, 10, 4, jax.nn.sigmoid)
# seed = 42
# key = jr.PRNGKey(seed)
# keys = jax.random.split(key, 4)
# activation_function = jax.nn.sigmoid
# layer = [
# eqx.nn.MLP(in_size='scalar', out_size='scalar', width_size=4, depth=2, activation=activation_function, key=keys[0]),
# # eqx.nn.MLP(in_size='scalar', out_size='scalar', width_size=4, depth=2, activation=activation_function, key=keys[1]),
# eqx.nn.Linear(in_features='scalar', out_features='scalar', key=keys[2])
# ]
# pinn.create_PINN_sequential(layer, seed)
# pinn.create_PINN_default(42, 10, 4, jax.nn.sigmoid)
seed = 42
key = jr.PRNGKey(seed)
keys = jax.random.split(key, 4)
activation_function = jax.nn.sigmoid
layer = [
eqx.nn.MLP(in_size='scalar', out_size='scalar', width_size=4, depth=2, activation=activation_function, key=keys[0]),
eqx.nn.Linear(in_features='scalar', out_features='scalar', key=keys[2])
]
pinn.create_PINN_sequential(layer, seed)
pinn.train()
plt.figure()
......
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# step, loss_train, loss_test, metrics_test
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src/train.dat 0 → 100644
+ 2701
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View file @ a8576079
This diff is collapsed.
temp.ipynb
+ 108
− 92
View file @ a8576079
Source diff could not be displayed: it is too large. Options to address this: view the blob.
test.dat deleted 100644 → 0
+ 0
− 181
View file @ da6e2e93
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