docs: update README

README.md

@@ -1,23 +1,62 @@
-# AIAA Applied Aerodynamics Surrogate Modeling - Case 4
-
-This folder contains the data available for the AIAA Applied Surrogate Modeling
-benchmark case 4 which consists out of in total 149 surface solutions solving
-the RANS equations. The corresponding AIAA SciTech2025 Paper provides more
-information about the dataset.
-
-The data is split in training and test samples in separat folders. All are
-available in two hdf5 files, one for training samples and one for test samples.
-These files provide parameter information (Mach, AoA, innerAileron,
-outerAileron, htp angle and elevator) and surface values (cp, cf, coords,
-normals, surface areas). An additional file is available providing connectivity
-information.
-
-Note that, if validation data is needed for your surrogate modeling technique
-these should be taken from the training samples.
-
-In addition to the data a simple python script *h5Import_NASA-CRM.py* is
-available. This file provides a function which reads the data from h5 file. It
-returns a dictionary with the sample numbers as keys. Under each key another 
-dictionary is available with global informations such as parameters values and
-global coefficients and surface solutions as numpy arrays.
-
+---
+license: mit
+
+---
+
+# <img src=".\assets\logo.png" alt="Logo" width="55" style="vertical-align: middle;">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.
+
+<p align="center">
+<img src=".\assets\GeoPT.png" height = "120" alt="" align=center />
+<br><br>
+</p>
+
+## Overview
+
+GeoPT is evaluated on the following five simulation tasks.
+
+| Dataset   | Mesh Size | Variable                       | Training | Test | Total Size | Source                                                       |
+| --------- | --------- | ------------------------------ | -------- | ---- | ---------- | ------------------------------------------------------------ |
+| DrivAerML | ~160M     | Geometry                       | 100      | 20   | ~6TB       | [Link][https://huggingface.co/datasets/neashton/drivaerml]   |
+| NASA-CRM  | ~450K     | Geometry, Speed, AoA           | 105      | 44   | ~3GB       | [Link](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO) |
+| AirCraft  | ~330K     | Geometry, Speed, AoA, Sideslip | 100      | 50   | ~7GB       | [Transolver++](https://arxiv.org/abs/2502.02414)             |
+| DTCHull   | ~240K     | Geometry, Yaw Angle            | 100      | 20   | ~2GB       | GeoPT                                                        |
+| Car-Crash | ~1M       | Impact Angle                   | 100      | 30   | ~8GB       | GeoPT                                                        |
+
+## Load Data
+
+```python
+from datasets import load_dataset
+load_dataset("GeoPT/Downstream_Physics_Simulation") # for AirCraft, DTCHull, Car-Crash, Radiosity
+load_dataset("neashton/drivaerml") # for DrivAerML
+```
+
+NASA-CRM can be obtained from [Google Drive](https://drive.google.com/drive/folders/1KhoZiEHlZhGI8omMwHrp2mZRKGiSAydO).
+
+## Examples
+
+<p align="center">
+<img src=".\assets\examples.png" height = "140" alt="" align=center />
+<br><br>
+</p>
+
+## Citation
+
+If you find this repo useful, please cite our 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:},
+  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).
+