| { |
| "train_dir": "/scratch/jtb3sud/processed_elasto_plastic/global_max/normalized/small/train", |
| "val_dir": "/scratch/jtb3sud/processed_elasto_plastic/global_max/normalized/small/val", |
| "file_pattern": "*.pt", |
| "seq_len": 16, |
| "stride": 16, |
| "num_static_feats": 2, |
| "num_dynamic_feats": 2, |
| "hidden_dim": 128, |
| "processor_size": 4, |
| "num_harmonics": 5, |
| "num_layers_node_processor": 2, |
| "num_layers_edge_processor": 2, |
| "num_layers_edge_encoder": 2, |
| "num_layers_node_decoder": 2, |
| "aggregation": "sum", |
| "mlp_activation": "relu", |
| "mask_eroding": true, |
| "use_loss_decay": false, |
| "loss_decay_gamma": 0.9, |
| "ss_schedule": "linear", |
| "ss_initial_ratio": 0.0, |
| "ss_final_ratio": 0.0, |
| "epochs": 1500, |
| "lr": 5e-05, |
| "grad_clip_norm": 1.0, |
| "device": "cuda", |
| "num_workers": 4, |
| "output_dir": "/scratch/jtb3sud/delta/elasto", |
| "resume": null, |
| "reset_best": false, |
| "fresh_scheduler": false, |
| "architecture": "MeshGraphKAN (NVIDIA PhysicsNeMo, PyG reimplementation)", |
| "kan_implementation": "Learnable Fourier coefficients [2, out, in, harmonics] with einsum", |
| "normalization": "global_max", |
| "input_dim_nodes": 4, |
| "input_dim_edges": 3, |
| "references": [ |
| "Pfaff et al., Learning Mesh-Based Simulation with Graph Networks, 2021", |
| "Liu et al., KAN: Kolmogorov-Arnold Networks, 2024", |
| "Peng et al., Interpretable physics-informed GNNs for flood forecasting, 2024", |
| "NVIDIA PhysicsNeMo: github.com/NVIDIA/physicsnemo" |
| ] |
| } |
