Add links to paper, GitHub, project page, and metadata
Browse filesThis PR improves the dataset card by:
- Adding the `task_categories: ['other']` to the YAML metadata.
- Including links to the official project page, the paper (Hugging Face Papers), and the GitHub repository.
- Organizing the content for better readability while maintaining the original overview and sample usage.
README.md
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---
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license: mit
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tags:
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- physics-simulation
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- physics-foundation-model
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# GeoPT
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GeoPT is a unified model pre-trained on large-scale geometric data for general physics simulation, unlocking a scalable path for neural simulation.
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```python
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from datasets import load_dataset
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```
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NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO).
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If you find this repo useful, please cite our paper.
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```
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@article{wu2026GeoPT,
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title={GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training},
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author={Haixu Wu, Minghao Guo, Zongyi Li, Zhiyang Dou, Mingsheng Long, Kaiming He, Wojciech Matusik},
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year={2026}
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}
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```
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---
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license: mit
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task_categories:
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- other
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tags:
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- physics-simulation
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- physics-foundation-model
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# GeoPT
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[Project Page](https://physics-scaling.github.io/GeoPT/) | [Paper](https://huggingface.co/papers/2602.20399) | [GitHub](https://github.com/Physics-Scaling/GeoPT)
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This repository contains the physics simulation data for the paper **GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training**.
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GeoPT is a unified model pre-trained on large-scale geometric data for general physics simulation, unlocking a scalable path for neural simulation.
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```python
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from datasets import load_dataset
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# For AirCraft, DTCHull, Car-Crash, Radiosity
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load_dataset("GeoPT/Downstream_Physics_Simulation")
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# For DrivAerML
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load_dataset("neashton/drivaerml")
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```
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NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO).
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If you find this repo useful, please cite our paper.
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```bibtex
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@article{wu2026GeoPT,
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title={GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training},
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author={Haixu Wu, Minghao Guo, Zongyi Li, Zhiyang Dou, Mingsheng Long, Kaiming He, Wojciech Matusik},
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journal={arXiv preprint arXiv:2602.20399},
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year={2026}
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}
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```
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