'poly_order': 2,
'diff_order': 3
}
}
n_runs = 5
for run_idx in np.arange(n_runs):
X_train, y_train, rand_idx = dataset.create_dataset(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
n_samples=1000,
noise=0.1,
random=True,
return_idx=True)
theta = dataset.library(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
poly_order=2,
deriv_order=3)[rand_idx, :]
dt = dataset.time_deriv(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1))[rand_idx, :]
model = DeepMod(**config)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.99, 0.999),
amsgrad=True)
train(model,
X_train,
y_train,
optimizer,
20000,
loss_func_args={
'library': torch.tensor(theta),
import numpy as np from phimal_utilities.data.burgers import BurgersDelta from phimal_utilities.data.diffusion import DiffusionGaussian from phimal_utilities.data import Dataset x = np.linspace(-1, 1, 50) t = np.linspace(1e-4, 5, 20) x_grid, t_grid = np.meshgrid(x, t, indexing='ij') v = 0.1 A = 1.0 dataset = Dataset(DiffusionGaussian, D=1.0, sigma=0.1, x0=-0.1) dataset.library(x_grid.reshape(-1, 1), t_grid.reshape(-1, 1))
