Add links to paper, GitHub, project page, and metadata

README.md

@@ -1,5 +1,7 @@
 ---
 license: mit
+task_categories:
+- other
 tags:
 - physics-simulation
 - physics-foundation-model
@@ -7,7 +9,9 @@ tags:
 
 # GeoPT
 
-This repository contains the physics simulation data 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)
+
+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.
 
@@ -31,8 +35,12 @@ GeoPT is evaluated on the following five simulation tasks.
 
 ```python
 from datasets import load_dataset
-load_dataset("GeoPT/Downstream_Physics_Simulation") # for AirCraft, DTCHull, Car-Crash, Radiosity
-load_dataset("neashton/drivaerml") # for DrivAerML
+
+# For AirCraft, DTCHull, Car-Crash, Radiosity
+load_dataset("GeoPT/Downstream_Physics_Simulation") 
+
+# For DrivAerML
+load_dataset("neashton/drivaerml") 
 ```
 
 NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO).
@@ -47,11 +55,11 @@ NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/fold
 
 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},
+  journal={arXiv preprint arXiv:2602.20399},
   year={2026}
 }
 ```