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PDEBench Datasets - Dataset - B2FIND
- b2find.eudat.eu
Updated Jan 20, 2025
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EmailClick to copy linkLink copiedCite(2025). PDEBench Datasets - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/17b50fa6-3602-55c7-90fb-b16a53200028Dataset updatedJan 20, 2025LicenseMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automaticallyDescriptionThis 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. This version includes the debugged version of advection and Burgers equation solutions.
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PDEBench Datasets
- darus.uni-stuttgart.de
- opendatalab.com
Updated Feb 13, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMakoto Takamoto; Timothy Praditia; Raphael Leiteritz; Dan MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (2024). PDEBench Datasets [Dataset]. http://doi.org/10.18419/DARUS-2986CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Unique identifierhttps://doi.org/10.18419/DARUS-2986Dataset updatedFeb 13, 2024Dataset provided byDaRUSAuthorsMakoto Takamoto; Timothy Praditia; Raphael Leiteritz; Dan MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias NiepertLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDataset funded byDFGDescriptionThis 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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FacebookTwitterEmailMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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. This version includes the debugged version of advection and Burgers equation solutions.