Regression on System of ODEs illustrative example 1 (train)
0.0002MSE (Data)DAEHN
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| DAEHNBest Epoch=3150, num_epochs=5000, model_depth=4, hidden_dim=32, lr=0.001, num_points=1500 out of 1500, pinn_reg_factor=1, hardnet_reg_factor=1, taylor_offset=0.1, taylor_order=1, eta=0.01, newton_step_length=1, max_newton_iter=10, noise_std=1, noise_mean=0, noise_scale=0.012025.12 | 0.0002 | 0.0004 | 0 | |
| MLPBest Epoch=2850, num_epochs=5000, model_depth=4, hidden_dim=32, lr=0.001, num_points=1500 out of 1500, pinn_reg_factor=1, hardnet_reg_factor=1, taylor_offset=0.1, taylor_order=1, eta=0.01, newton_step_length=1, max_newton_iter=10, noise_std=1, noise_mean=0, noise_scale=0.012025.12 | 0.0003 | — | 2.6 | |
| PINNBest Epoch=4120, num_epochs=5000, model_depth=4, hidden_dim=32, lr=0.001, num_points=1500 out of 1500, pinn_reg_factor=1, hardnet_reg_factor=1, taylor_offset=0.1, taylor_order=1, eta=0.01, newton_step_length=1, max_newton_iter=10, noise_std=1, noise_mean=0, noise_scale=0.012025.12 | 0.0017 | — | 0.16 |