%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 EXC2075 PN6 PN6-2 curated %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 = {2024-09-30T18:20:11.000+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/22b58ea3f5c19661e59e9fe0e6cac098d/simtech}, doi = {10.18419/darus-2987}, howpublished = {Dataset}, interhash = {b673577efe95c0d908359b2178b8b64d}, intrahash = {2b58ea3f5c19661e59e9fe0e6cac098d}, keywords = {EXC2075 PN6 PN6-2 curated}, 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 = {2025-01-27T13:14:14.000+0100}, title = {PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"}, year = 2022 }