---
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")
```