%0 Generic %1 takamoto2022pdebench %A Takamoto, Makoto %A Praditia, Timothy %A Leiteritz, Raphael %A MacKinlay, Dan %A Alesiani, Francesco %A Pflüger, Dirk %A Niepert, Mathias %D 2022 %K %R 10.18419/darus-2986 %T PDEBench Datasets : Data for "PDEBench: An Extensive Benchmark for Scientific Machine Learning" %X 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.

@misc{takamoto2022pdebench, abstract = {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. }, added-at = {2023-09-25T15:40:34.000+0200}, affiliation = {Takamoto, Makoto/NEC Labs Europe, Praditia, Timothy/Universität Stuttgart, Leiteritz, Raphael/Universität Stuttgart, MacKinlay, Dan/CSIRO's Data61, Alesiani, Francesco/NEC Labs Europe, Pflüger, Dirk/Universität Stuttgart, Niepert, Mathias/Universität Stuttgart}, author = {Takamoto, Makoto and Praditia, Timothy and Leiteritz, Raphael and MacKinlay, Dan and Alesiani, Francesco and Pflüger, Dirk and Niepert, Mathias}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20613996a65531e524bcf5f5307bf1959/unibiblio}, doi = {10.18419/darus-2986}, howpublished = {Dataset}, interhash = {1bcd4393f796302bb21cf01fc7b1837a}, intrahash = {0613996a65531e524bcf5f5307bf1959}, keywords = {}, note = {Related to: 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. arXiv: 2210.07182}, orcid-numbers = {Takamoto, Makoto/0000-0001-7192-1956, Praditia, Timothy/0000-0003-3619-9122, Leiteritz, Raphael/0000-0001-8070-2384, MacKinlay, Dan/0000-0001-6077-2684, Alesiani, Francesco/0000-0003-4413-7247, Pflüger, Dirk/0000-0002-4360-0212, Niepert, Mathias/0000-0002-8401-3751}, timestamp = {2023-09-25T13:40:34.000+0200}, title = {PDEBench Datasets : Data for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"}, year = 2022 }