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introvoyz041/Downstream_Physics_Simulation

GeoPT Project Page | Paper | GitHub 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… See the full description on the dataset page: https://huggingface.co/datasets/introvoyz041/Downstream_Physics_Simulation.

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GeoPT

Project Page | Paper | GitHub

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.

DatasetMesh SizeVariableTrainingTestTotal SizeSource
DrivAerML~160MGeometry10020~6TBLink
NASA-CRM~450KGeometry, Speed, AoA10544~3GBLink
AirCraft~330KGeometry, Speed, AoA, Sideslip10050~7GBTransolver++
DTCHull~240KGeometry, Yaw Angle10020~2GBGeoPT
Car-Crash~1MImpact Angle10030~8GBGeoPT

Load Data

python
from datasets import load_dataset

# For AirCraft, DTCHull, Car-Crash
load_dataset("GeoPT/Downstream_Physics_Simulation") 

# For DrivAerML
load_dataset("neashton/drivaerml")

NASA-CRM can be obtained from Google Drive.

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.

bibtex
@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).