| license: mit | |
| tags: | |
| - physics-ml | |
| - graph-neural-networks | |
| - computational-mechanics | |
| - elastoplastic | |
| # G-PARC: Graph Physics-Aware Recurrent Convolutions | |
| Model weights, test data, and configuration files for the G-PARC elastoplastic simulation paper. | |
| ## Models | |
| | Model | Description | | |
| |-------|-------------| | |
| | G-PARCv1 | Graph Physics-Aware Recurrent Convolutions — fully learned GNN operators | | |
| | G-PARCv2 | MLS differential operators + numerical Euler integration | | |
| | MeshGraphKAN | Kolmogorov-Arnold Network message passing with Fourier basis | | |
| | MeshGraphNet | Standard encode-process-decode GNN (Pfaff et al., 2021) | | |
| ## Dataset | |
| PLAID 2D Elasto-Plasto-Dynamics benchmark — high-velocity impact on steel plates. | |
| - **Variables**: Displacement field (U_x, U_y) | |
| - **Normalization**: Global max | |
| - **Meshes**: Unstructured quad elements | |
| ## Usage | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| ckpt = hf_hub_download("jacktbeerman/Gparc", "checkpoints/gparcv2_best.pth") | |
| ``` | |