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train dict |
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{"f":0.03864646330475807,"k":0.061626262962818146,"seed":0,"states":[[[[0.949999988079071,0.88685375(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":1,"states":[[[[0.949999988079071,0.99035894(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":2,"states":[[[[0.949999988079071,0.99035888(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":3,"states":[[[[0.949999988079071,0.98951148(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":4,"states":[[[[0.949999988079071,0.98343223(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":5,"states":[[[[0.949999988079071,0.99028867(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":6,"states":[[[[0.949999988079071,0.99034887(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":7,"states":[[[[0.949999988079071,0.99035137(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":8,"states":[[[[0.949999988079071,0.98959475(...TRUNCATED) |
{"f":0.03864646330475807,"k":0.061626262962818146,"seed":9,"states":[[[[0.949999988079071,0.99016910(...TRUNCATED) |
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2D Gray-Scott Reaction-Diffusion Dataset
2d gray-scott reaction-diffusion dataset. The underlying PDE is the Gray-Scott reaction-diffusion.
Data is stored in HDF5 (.h5) format.
Origin
This dataset was generated by Armand Kassai Koupaï and has been used in the following papers:
- ZEBRA: In-Context Generative Pretraining for Solving Parametric PDEs — Louis Serrano, Armand Kassaï Koupaï, Thomas X Wang, Pierre Erbacher, Patrick Gallinari. ICML 2025. OpenReview
- ENMA: Tokenwise Autoregression for Generative Neural PDE Operators — Armand Kassaï Koupaï, Lise Le Boudec, Louis Serrano, Patrick Gallinari. NeurIPS 2025.
Download
See the full download script in the Zebra repository.
pip install huggingface_hub
python download_data/download_data_hugging_face.py --datasets gs
Usage
import h5py
data = h5py.File("train.h5", "r")
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