PDEBench Datasetsdoi:10.18419/darus-2986DaRUS2022-07-268Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/darus-2986, DaRUS, V8PDEBench DatasetsData for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"doi:10.18419/darus-2986Takamoto, MakotoPraditia, TimothyLeiteritz, RaphaelMacKinlay, DanAlesiani, FrancescoPflüger, DirkNiepert, MathiasTakamoto, MakotoPraditia, TimothyLeiteritz, RaphaelMacKinlay, DanAlesiani, FrancescoPflüger, DirkNiepert, MathiasEXC 2075 - 390740016DaRUSLeiteritz, RaphaelTakamoto, MakotoTakamoto, MakotoMacKinlay, DanPraditia, TimothyAlesiani, FrancescoLeiteritz, Raphael2022-06-08Computer and Information ScienceEarth and Environmental SciencesPhysicsBenchmarkScientific Machine LearningMachine LearningPhysics-Informed Machine LearningPartial Differential Equation<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>HDF5 filesThis version includes the debugged version of advection and Burgers equation solutions.Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: <a href="https://github.com/pdebench/PDEBench">PDEBench</a>. Github repository. 2022Makoto, T., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M. (2022): PDEBench Pretrained Models, <a href="https://doi.org/10.18419/darus-2987">doi: 10.18419/darus-2987</a>, DaRUS.Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.2210.07182Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and 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conditionapplication/x-hdf52D_CFD_Rand_M0.1_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf52D CFD Train data for random initial conditionapplication/x-hdf52D_CFD_Rand_M1.0_Eta0.01_Zeta0.01_periodic_128_Train.hdf52D CFD Train data for random initial conditionapplication/x-hdf52D_CFD_Rand_M1.0_Eta0.1_Zeta0.1_periodic_128_Train.hdf52D CFD Train data for random initial conditionapplication/x-hdf52D_CFD_Rand_M1.0_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf52D CFD Train data for random initial conditionapplication/x-hdf52D_CFD_Turb_M0.1_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf52D CFD Train data for Turbulence initial conditionapplication/x-hdf52D_CFD_Turb_M1.0_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf52D CFD Train data for Turbulence initial conditionapplication/x-hdf52D_DarcyFlow_beta0.01_Train.hdf5DarcyFlow dataset (2D)application/x-hdf52D_DarcyFlow_beta0.1_Train.hdf5DarcyFlow dataset (2D)application/x-hdf52D_DarcyFlow_beta1.0_Train.hdf5DarcyFlow dataset (2D)application/x-hdf52D_DarcyFlow_beta10.0_Train.hdf5DarcyFlow dataset (2D)application/x-hdf52D_DarcyFlow_beta100.0_Train.hdf5DarcyFlow dataset 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