| # randomness | |
| seed: 91 # 91 | |
| # dataset | |
| generator: | |
| # options: "bezier_symmetric_double", "bezier_symmetric", "bezier_asymmetric", "bezier_lerp" | |
| name: "bezier_symmetric_double" # "bezier_symmetric_double" | |
| bounds: "saddle" # options: pillow, dome, saddle | |
| num_uv: 10 # 10, 16, 23 | |
| size: 10.0 | |
| num_points: 4 # grid points | |
| lerp_factor: 0.5 # scalar factor in [0, 1] to interpolate between 2 surfaces, only for bezier_lerp | |
| # simulator | |
| fdm: | |
| load: -0.5 # -0.5, scale of vertical area load | |
| # neural networks | |
| encoder: | |
| shift: 0.0 | |
| hidden_layer_size: 256 | |
| hidden_layer_num: 3 | |
| activation_fn_name: "elu" | |
| final_activation_fn_name: "softplus" # needs softplus to ensure positive output | |
| decoder: | |
| # If true, the decoder maps (z, boundary conditions) -> x. Otherwise, z -> x. | |
| include_params_xl: True | |
| hidden_layer_size: 256 | |
| hidden_layer_num: 3 | |
| activation_fn_name: "elu" | |
| # loss function | |
| loss: | |
| shape: | |
| include: True | |
| weight: 1.0 # weight of the shape error term in the loss function | |
| residual: # PINN term | |
| include: True | |
| weight: 1.0 # weight of the residual error term in the loss function | |
| # optimization | |
| optimizer: | |
| name: "adam" | |
| learning_rate: 3.0e-5 # 3.0e-5 (formfinder), 5.0e-5 (others). Be careful with scientific notation in YAML! | |
| clip_norm: 0.0 | |
| # training | |
| training: | |
| steps: 10000 # 10000 | |
| batch_size: 64 | |