Datasets:

sogeeking
/

burgers-nu-forcing-2

test dict
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[0.3336703748820601,0.3418145508(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-1.1416017278732098,-1.14497367(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.5084460565455575,-0.52125877(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.04093095634417729,-0.0220172(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.07165685444176717,-0.0996509(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[0.12030308047716887,0.135807046(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.08936019474372793,-0.0805006(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.49573802704706893,-0.4897994(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[-0.43883224345263677,-0.4277454(...TRUNCATED)
{"alpha":1.0,"beta":0.013987024908081572,"gamma":0.0,"pde_250-256":[[0.1678009480852934,0.1551527794(...TRUNCATED)

1D Burgers Equation Dataset With Varying Viscosity And Forcing

1d burgers equation dataset with varying viscosity and forcing. The underlying PDE is the Burgers equation with varying viscosity and forcing.

Data is stored in HDF5 (.h5) format.

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.

See the full download script in the Zebra repository.

pip install huggingface_hub

python download_data/download_data_hugging_face.py --datasets burgers_nu_forcing_2

import h5py

data = h5py.File("train.h5", "r")