license: mit
pipeline_tag: other
tags:
- physics-simulation
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
Project Page | Paper | GitHub
GeoPT is a unified pre-trained model for general physics simulation based on lifted geometric pre-training. It bridges the geometry-physics gap by augmenting geometry with synthetic dynamics, enabling dynamics-aware self-supervision without the need for expensive physics labels.
Key Features
- Data Efficiency: Reduces labeled training data requirements by 20–60% across diverse physics simulation tasks.
- Scalable Self-Supervision: Generates millions of training samples quickly, significantly faster than traditional physics supervision.
- Strong Scaling: Performance consistently improves with larger models and more training data.
- Generalization: Generalizes across diverse physical systems (fluid and solid mechanics) by reconfiguring the dynamics condition as a "prompt".
Usage
Concrete usage instructions, including environment setup and scripts for fine-tuning GeoPT on various industrial-fidelity benchmarks (such as AirCraft, Cars, and Ships), can be found in the official GitHub repository.
Citation
If you find this repo useful, please cite the 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, please contact Haixu Wu (wuhaixu98@gmail.com) and Minghao Guo (guomh2014@gmail.com).
Acknowledgement
We appreciate the following GitHub repositories for their valuable codebase or datasets: Transolver and Neural-Solver-Library.