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train dict |
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{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
{"alpha":0.0,"beta":0.3371389492810266,"dt":0.07194244604316546,"dx":0.0625,"gamma":0.0,"nu":0.03676(...TRUNCATED) |
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1D Advection-Diffusion Equation Dataset
1d advection-diffusion equation dataset. The underlying PDE is the advection-diffusion equation.
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 advection_diffusion
Usage
import h5py
data = h5py.File("train.h5", "r")
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