Add model card and metadata

README.md

@@ -1,24 +1,32 @@
 ---
 license: mit
+pipeline_tag: other
 tags:
 - physics-simulation
 ---
 
-# GeoPT
+# GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
 
-This repository contains pre-trained models for the paper GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training.
+[**Project Page**](https://physics-scaling.github.io/GeoPT/) | [**Paper**](https://huggingface.co/papers/2602.20399) | [**GitHub**](https://github.com/Physics-Scaling/GeoPT)
 
-GeoPT is a unified model pre-trained on large-scale geometric data for general physics simulation, unlocking a scalable path for neural simulation.
+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 can be found in https://github.com/Physics-Scaling/GeoPT
+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](https://github.com/Physics-Scaling/GeoPT).
 
 ## Citation
 
-If you find this repo useful, please cite our paper. 
+If you find this repo useful, please cite the paper:
 
-```
+```latex
 @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},
@@ -29,4 +37,8 @@ If you find this repo useful, please cite our paper.
 
 ## 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).
+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](https://github.com/thuml/Transolver) and [Neural-Solver-Library](https://github.com/thuml/Neural-Solver-Library).