10.18419/darus-2986Takamoto, MakotoMakotoTakamoto0000-0001-7192-1956NEC Labs EuropePraditia, TimothyTimothyPraditia0000-0003-3619-9122Universität StuttgartLeiteritz, RaphaelRaphaelLeiteritz0000-0001-8070-2384Universität StuttgartMacKinlay, DanDanMacKinlay0000-0001-6077-2684CSIRO's Data61Alesiani, FrancescoFrancescoAlesiani0000-0003-4413-7247NEC Labs EuropePflüger, DirkDirkPflüger0000-0002-4360-0212Universität StuttgartNiepert, MathiasMathiasNiepert0000-0002-8401-3751Universität StuttgartPDEBench DatasetsData for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"DaRUS2022Computer and Information ScienceEarth and Environmental SciencesPhysicsBenchmarkScientific Machine LearningMachine LearningPhysics-Informed Machine LearningPartial Differential EquationLeiteritz, RaphaelRaphaelLeiteritzUniversität StuttgartTakamoto, MakotoMakotoTakamotoNEC Labs EuropeTakamoto, MakotoMakotoTakamotoNEC Labs EuropeMacKinlay, DanDanMacKinlayCSIRO's Data61Praditia, TimothyTimothyPraditiaUniversität StuttgartAlesiani, FrancescoFrancescoAlesianiNEC Labs EuropeTakamoto, MakotoMakotoTakamotoPraditia, TimothyTimothyPraditiaLeiteritz, RaphaelRaphaelLeiteritzMacKinlay, DanDanMacKinlayAlesiani, FrancescoFrancescoAlesianiPflüger, DirkDirkPflügerNiepert, MathiasMathiasNiepert2022-06-082024-02-13HDF5 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BY 4.0<p>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).</p>
<p>More detailed information are also provided in our Github repository (<a href="https://github.com/pdebench/PDEBench">https://github.com/pdebench/PDEBench</a>) and our submitting paper to NeurIPS 2022 Benchmark track.</p>This version includes the debugged version of advection and Burgers equation solutions.DFGEXC 2075 - 390740016