%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 darus mult ubs_10002 ubs_10005 ubs_10021 ubs_20002 ubs_20008 ubs_20019 ubs_30028 ubs_30082 ubs_30165 ubs_40041 ubs_40349 ubs_40434 unibibliografie %R 10.18419/darus-2987 %T PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning" %X This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 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. In addition the dataset contains the pre-trained model for the 1D Inverse problem for FNO and U-Net. These models are stored using the same structure as the dataset they trained on. All the files are saved in .pt files, the default file type for the PyTorch library.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 the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 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. In addition the dataset contains the pre-trained model for the 1D Inverse problem for FNO and U-Net. These models are stored using the same structure as the dataset they trained on. All the files are saved in .pt files, the default file type for the PyTorch library.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 = {2022-07-01T11:31:44.000+0200}, affiliation = {Takamoto, Makoto/NEC Labs Europe, Praditia, Timothy/University of Stuttgart, Leiteritz, Raphael/University of Stuttgart, MacKinlay, Dan/CSIRO's Data61, Alesiani, Francesco/NEC Labs Europe, Pflüger, Dirk/University of Stuttgart, Niepert, Mathias/University of 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/22b58ea3f5c19661e59e9fe0e6cac098d/unibiblio}, doi = {10.18419/darus-2987}, howpublished = {Dataset}, interhash = {b673577efe95c0d908359b2178b8b64d}, intrahash = {2b58ea3f5c19661e59e9fe0e6cac098d}, keywords = {darus mult ubs_10002 ubs_10005 ubs_10021 ubs_20002 ubs_20008 ubs_20019 ubs_30028 ubs_30082 ubs_30165 ubs_40041 ubs_40349 ubs_40434 unibibliografie}, 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}, 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 = {2025-03-03T09:55:19.000+0100}, title = {PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"}, year = 2022 }