GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
Paper
• 2602.20399 • Published
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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.
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 |
| NASA-CRM | ~450K | Geometry, Speed, AoA | 105 | 44 | ~3GB | Link |
| AirCraft | ~330K | Geometry, Speed, AoA, Sideslip | 100 | 50 | ~7GB | Transolver++ |
| DTCHull | ~240K | Geometry, Yaw Angle | 100 | 20 | ~2GB | GeoPT |
| Car-Crash | ~1M | Impact Angle | 100 | 30 | ~8GB | GeoPT |
Load Data
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.
Examples
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).
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