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val dict |
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{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000000,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000001,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000002,"states":[[[[0.949999988079071,0.98(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000003,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000004,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000005,"states":[[[[0.949999988079071,0.92(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000006,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000007,"states":[[[[0.0,0.2239655256271362(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000008,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
{"f":0.04548484832048416,"k":0.057121213525533676,"seed":1000009,"states":[[[[0.949999988079071,0.99(...TRUNCATED) |
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Out-Of-Distribution Evaluation Set For The 2D Gray-Scott Reaction-Diffusion Equation
Out-of-distribution evaluation set for the 2d gray-scott reaction-diffusion equation. The underlying PDE is the Gray-Scott reaction-diffusion.
This dataset contains out-of-distribution (OOD) samples for evaluating model generalization.
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_ood
Usage
import h5py
data = h5py.File("train.h5", "r")
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