---
license: mit
tags:
- physics-simulation
- physics-foundation-model
---

# GeoPT

This repository contains the physics simulation data for the paper GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training.

GeoPT is a unified model pre-trained on large-scale geometric data for general physics simulation, unlocking a scalable path for neural simulation.

<p align="center">
<img src="assets/GeoPT.png" height = "120" alt="" align=center />
</p>

## Overview

GeoPT is evaluated on the following five simulation tasks.

| Dataset   | Mesh Size | Variable                       | Training | Test | Total Size | Source                                                       |
| --------- | --------- | ------------------------------ | -------- | ---- | ---------- | ------------------------------------------------------------ |
| DrivAerML | ~160M     | Geometry                       | 100      | 20   | ~6TB       | [Link](https://huggingface.co/datasets/neashton/drivaerml)   |
| NASA-CRM  | ~450K     | Geometry, Speed, AoA           | 105      | 44   | ~3GB       | [Link](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO) |
| AirCraft  | ~330K     | Geometry, Speed, AoA, Sideslip | 100      | 50   | ~7GB       | [Transolver++](https://arxiv.org/abs/2502.02414)             |
| DTCHull   | ~240K     | Geometry, Yaw Angle            | 100      | 20   | ~2GB       | GeoPT                                                        |
| Car-Crash | ~1M       | Impact Angle                   | 100      | 30   | ~8GB       | GeoPT                                                        |

## Load Data

```python
from datasets import load_dataset
load_dataset("GeoPT/Downstream_Physics_Simulation") # for AirCraft, DTCHull, Car-Crash, Radiosity
load_dataset("neashton/drivaerml") # for DrivAerML
```

NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO).

## Examples

<p align="center">
<img src="assets/examples.png" height = "140" alt="" align=center />
</p>

## Citation

If you find this repo useful, please cite our paper. 

```
@article{wu2026GeoPT,
  title={GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training},
  author={Haixu Wu, Minghao Guo, Zongyi Li, Zhiyang Dou, Mingsheng Long, Kaiming He, Wojciech Matusik},
  booktitle={arXiv preprint arXiv:2602.20399},
  year={2026}
}
```

## Contact

If you have any questions or want to use the code, please contact Haixu Wu (wuhaixu98@gmail.com) and Minghao Guo (guomh2014@gmail.com).