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Part 1: Document Description
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Citation |
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Title: |
PDEBench Datasets |
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Identification Number: |
doi:10.18419/darus-2986 |
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Distributor: |
DaRUS |
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Date of Distribution: |
2022-07-26 |
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Version: |
8 |
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Bibliographic Citation: |
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/darus-2986, DaRUS, V8 |
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Citation |
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Title: |
PDEBench Datasets |
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Subtitle: |
Data for "PDEBench: An Extensive Benchmark for Scientific Machine Learning" |
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Identification Number: |
doi:10.18419/darus-2986 |
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Authoring Entity: |
Takamoto, Makoto (NEC Labs Europe) |
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Praditia, Timothy (Universität Stuttgart) |
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Leiteritz, Raphael (Universität Stuttgart) |
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MacKinlay, Dan (CSIRO's Data61) |
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Alesiani, Francesco (NEC Labs Europe) |
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Pflüger, Dirk (Universität Stuttgart) |
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Niepert, Mathias (Universität Stuttgart) |
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Other identifications and acknowledgements: |
Takamoto, Makoto |
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Other identifications and acknowledgements: |
Praditia, Timothy |
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Other identifications and acknowledgements: |
Leiteritz, Raphael |
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Other identifications and acknowledgements: |
MacKinlay, Dan |
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Other identifications and acknowledgements: |
Alesiani, Francesco |
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Other identifications and acknowledgements: |
Pflüger, Dirk |
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Other identifications and acknowledgements: |
Niepert, Mathias |
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Grant Number: |
EXC 2075 - 390740016 |
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Distributor: |
DaRUS |
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Access Authority: |
Leiteritz, Raphael |
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Access Authority: |
Takamoto, Makoto |
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Access Authority: |
Takamoto, Makoto |
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Access Authority: |
MacKinlay, Dan |
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Access Authority: |
Praditia, Timothy |
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Access Authority: |
Alesiani, Francesco |
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Depositor: |
Leiteritz, Raphael |
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Date of Deposit: |
2022-06-08 |
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Holdings Information: |
https://doi.org/10.18419/darus-2986 |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Physics, Benchmark, Scientific Machine Learning, Machine Learning, Physics-Informed Machine Learning, Partial Differential Equation |
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Abstract: |
<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> |
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Kind of Data: |
HDF5 files |
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Notes: |
This version includes the debugged version of advection and Burgers equation solutions. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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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. 2022 |
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Related Studies |
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Makoto, 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. |
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Related Publications |
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Citation |
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Title: |
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. |
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Identification Number: |
2210.07182 |
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Bibliographic Citation: |
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. |
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1D_CFD_Rand_Eta0.01_Zeta0.01_periodic_Train.hdf5 |
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1D CFD Train data for random initial condition |
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1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5 |
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1D CFD Train data for random initial condition |
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1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic_Train.hdf5 |
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1D CFD Train data for random initial condition |
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1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_trans_Train.hdf5 |
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CFD dataset (1D) |
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CFD dataset (1D) |
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CFD dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReacDiff_Nu1.0_Rho5.0.hdf5 |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReacDiff_Nu2.0_Rho2.0.hdf5 |
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ReactionDiffusion dataset (1D) |
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ReacDiff_Nu2.0_Rho5.0.hdf5 |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReactionDiffusion dataset (1D) |
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ReacDiff_Nu5.0_Rho2.0.hdf5 |
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ReactionDiffusion dataset (1D) |
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ReacDiff_Nu5.0_Rho5.0.hdf5 |
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ReactionDiffusion dataset (1D) |
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2D_CFD_Rand_M0.1_Eta0.01_Zeta0.01_periodic_128_Train.hdf5 |
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2D_CFD_Rand_M0.1_Eta0.1_Zeta0.1_periodic_128_Train.hdf5 |
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2D_CFD_Rand_M0.1_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf5 |
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2D CFD Train data for random initial condition |
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2D_CFD_Rand_M1.0_Eta0.01_Zeta0.01_periodic_128_Train.hdf5 |
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2D_CFD_Rand_M1.0_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf5 |
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2D CFD Train data for random initial condition |
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2D_CFD_Turb_M0.1_Eta1e-08_Zeta1e-08_periodic_512_Train.hdf5 |
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