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Croissant
Croissant is a format for Machine Learning datasets
Learn more about this at mlcommons.org/croissant.
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100+ datasets found
  1. D

    PDEBench Datasets

    • darus.uni-stuttgart.de
    • resodate.org
    Updated Feb 13, 2024
    + more versions
  2. D

    Research Data Management Project Form

    • darus.uni-stuttgart.de
    Updated Dec 11, 2019
  3. D

    First Steps with DaRUS

    • darus.uni-stuttgart.de
    Updated Sep 7, 2019
  4. D

    Data Management of a Biotechnology Network as a Contribution to FAIR Data...

    • darus.uni-stuttgart.de
    Updated Dec 16, 2022
  5. D

    Data from: Satellite Altimetry-based Extension of global-scale in situ river...

    • darus.uni-stuttgart.de
    • resodate.org
    Updated Feb 14, 2025
  6. D

    Image enhancement code: time-resolved tomograms of EICP application using 3D...

    • darus.uni-stuttgart.de
    • resodate.org
    Updated Feb 7, 2023
  7. D

    Replication data of Buchmeiser group for: "Room Temperature Ethene to...

    • darus.uni-stuttgart.de
    • search.nfdi4chem.de
    Updated Apr 14, 2025
  8. D

    Supplementary material for "GPU-accelerated classical density functional...

    • darus.uni-stuttgart.de
    • search.nfdi4chem.de
    • +1more
    Updated Mar 7, 2025
    + more versions
  9. D

    FLP Telemetry Data

    • darus.uni-stuttgart.de
    • resodate.org
    Updated Jun 23, 2021
    + more versions
  10. D

    Data repository for "Loss Behavior in Supervised Learning With Entangled...

    • darus.uni-stuttgart.de
    Updated Sep 12, 2025
  11. D

    Data for: Impact of N on the Stacking Fault Energy and Phase Stability of...

    • darus.uni-stuttgart.de
    • search.nfdi4chem.de
    Updated Jun 17, 2025
  12. D

    ANDroMeDA_UAV01_WINSENT_Test_Site

    • darus.uni-stuttgart.de
    • windlab.hlrs.de
    Updated Dec 2, 2024
  13. D

    3D CT-Data for DM_LN3_G200

    • darus.uni-stuttgart.de
    Updated Sep 1, 2021
    + more versions
  14. D

    Measurements of soil temperatures and moisture content

    • darus.uni-stuttgart.de
    Updated Aug 12, 2023
  15. D

    2D pictures of DM03-17

    • darus.uni-stuttgart.de
    • resodate.org
    Updated May 22, 2021
    + more versions
  16. D

    Data Repository for a Systematic Mapping Study on Warm-Starting and Quantum...

    • darus.uni-stuttgart.de
    Updated Mar 15, 2023
  17. D

    Measured hydrometeorologic data

    • darus.uni-stuttgart.de
    • windlab.hlrs.de
    Updated Aug 12, 2023
  18. D

    3D CT-Data for WZ03-09

    • darus.uni-stuttgart.de
    • resodate.org
    Updated May 21, 2021
    + more versions
  19. D

    2D pictures of WKI06/2-20

    • darus.uni-stuttgart.de
    • resodate.org
    Updated May 21, 2021
    + more versions
  20. D

    Relevance of Different Metadata Fields for the Description of Research Data...

    • darus.uni-stuttgart.de
    Updated Dec 20, 2019
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Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Dan MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (2024). PDEBench Datasets [Dataset]. http://doi.org/10.18419/DARUS-2986

PDEBench Datasets

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412 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Feb 13, 2024
Dataset provided by
DaRUS
Authors
Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Dan MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Dataset funded by
DFG
Description

This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This dataset is intended to progress the scientific ML research area. In general, the data are stored in HDF5 format, with the array dimensions packed according to the convention [b,t,x1,...,xd,v], where b is the batch size (i.e. number of samples), t is the time dimension, x1,...,xd are the spatial dimensions, and v is the number of channels (i.e. number of variables of interest). More detailed information are also provided in our Github repository (https://github.com/pdebench/PDEBench) and our submitting paper to NeurIPS 2022 Benchmark track.

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