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Part 1: Document Description
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Citation |
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Title: |
PDEBench Pretrained Models |
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Identification Number: |
doi:10.18419/darus-2987 |
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Distributor: |
DaRUS |
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Date of Distribution: |
2022-06-21 |
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Version: |
2 |
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Bibliographic Citation: |
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Pretrained Models", https://doi.org/10.18419/darus-2987, DaRUS, V2 |
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Citation |
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Title: |
PDEBench Pretrained Models |
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Subtitle: |
Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning" |
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Identification Number: |
doi:10.18419/darus-2987 |
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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: |
2022-06-09 |
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Holdings Information: |
https://doi.org/10.18419/darus-2987 |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Physics, Benchmark, Scientific Machine Learning, Physics-Informed Machine Learning, Machine Learning, Partial Differential Equation |
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Abstract: |
<p>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. <br/> All the files are saved in .pt files, the default file type for the PyTorch library.</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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Notes: |
This version includes the pretrained model weights trained using the latest Advection and Burgers equation data. |
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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 Datasets, <a href="https://doi.org/10.18419/darus-2986">doi: 10.18419/darus-2986</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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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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Label: |
1D_Advection_Sols_beta4.0_FNO.pt |
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Text: |
Advection pre-trained model (FNO) (1D) |
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Notes: |
application/octet-stream |
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1D_Advection_Sols_beta4.0_Unet.pt |
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Text: |
Advection pre-trained model (Unet) (1D) |
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Notes: |
application/octet-stream |
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Label: |
1D_Burgers_Sols_Nu1.0_FNO.pt |
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Text: |
Burgers pre-trained model (FNO) (1D) |
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Notes: |
application/octet-stream |
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Label: |
1D_Burgers_Sols_Nu1.0_Unet.pt |
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Text: |
Burgers pre-trained model (Unet) (1D) |
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Notes: |
application/octet-stream |
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Label: |
1D_CFD_Shock_trans_Train_FNO.pt |
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Text: |
CFD pre-trained model (FNO) (1D) |
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Notes: |
application/octet-stream |
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Label: |
1D_CFD_Shock_trans_Train_Unet.pt |
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Text: |
CFD pre-trained model (Unet) (1D) |
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Notes: |
application/octet-stream |
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Label: |
ReacDiff_Nu1.0_Rho2.0_FNO.pt |
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Text: |
ReactionDiffusion pre-trained model (FNO) (1D) |
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Notes: |
application/octet-stream |
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Label: |
ReacDiff_Nu1.0_Rho2.0_Unet.pt |
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Text: |
ReactionDiffusion pre-trained model (Unet) (1D) |
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Notes: |
application/octet-stream |
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Label: |
1DCFD_FNO.tar |
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Text: |
1D compressible NS eq pretrained model for FNO |
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Notes: |
application/x-tar |
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Label: |
1DReacDiff_FNO.tar |
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Text: |
1D Reaction-Diffusion eq pretrained model for FNO |
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Notes: |
application/x-tar |
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Label: |
1D_diff-sorp_NA_NA_FNO.pt |
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Notes: |
application/octet-stream |
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Label: |
2DCFD_FNO.tar |
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Text: |
2D compressible NS eq pretrained model for FNO |
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Notes: |
application/x-tar |
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2D_diff-react_NA_NA_FNO.pt |
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Notes: |
application/octet-stream |
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2D_rdb_NA_NA_FNO.pt |
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Notes: |
application/octet-stream |
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3DCFD_FNO.tar |
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Text: |
3D compressible NS eq pretrained model for FNO |
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Notes: |
application/x-tar |
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advection_FNO-1.tar |
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Text: | |
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text/plain |
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advection_FNO.tar |
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Text: |
1D Advection eq pretrained model for FNO |
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Notes: |
application/x-tar |
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Label: |
burgers_FNO-1.tar |
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Text: | |
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Notes: |
text/plain |
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burgers_FNO.tar |
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Text: |
1D Burgers eq pretrained model for FNO |
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Notes: |
application/x-tar |
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Label: |
DarcyFlow_FNO.tar |
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Text: |
2D DarcyFlow pretrained model for FNO |
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Notes: |
application/x-tar |
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Label: |
1DCFD_PINN.tar |
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Text: |
1D compressible NS eq pretrained model for PINN |
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Notes: |
application/x-tar |
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Label: |
1DReacDiff_PINN.tar |
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Text: |
1D Reaction-Diffusion eq pretrained model for PINN |
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Notes: |
application/x-tar |
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Label: |
1D_diff-sorp_NA_NA_0001.h5_PINN.pt-15000.pt |
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Notes: |
application/vnd.snesdev-page-table |
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2D_diff-react_NA_NA_0000.h5_PINN.pt-15000.pt |
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Notes: |
application/vnd.snesdev-page-table |
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2D_rdb_NA_NA_0000.h5_PINN.pt-15000.pt |
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Notes: |
application/vnd.snesdev-page-table |
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Label: |
advection_PINN-1.tar |
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Text: | |
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Notes: |
text/plain |
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advection_PINN.tar |
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Text: |
1D Advection eq pretrained model for PINN |
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Notes: |
application/x-tar |
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Label: |
burgers_PINN-1.tar |
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Notes: |
text/plain |
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Label: |
burgers_PINN.tar |
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Text: |
1D Burgers eq pretrained model for PINN |
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Notes: |
application/x-tar |
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Label: |
1DCFD_Unet.tar |
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Text: |
1D compressible NS eq pretrained model for Unet |
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Notes: |
application/x-tar |
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Label: |
1DReacDiff_Unet.tar |
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Text: |
1D Reaction-Diffusion eq pretrained model for Unet |
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Notes: |
application/x-tar |
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Label: |
1D_diff-sorp_NA_NA_Unet-1-step.pt |
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Notes: |
application/octet-stream |
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1D_diff-sorp_NA_NA_Unet-AR.pt |
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Notes: |
application/octet-stream |
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1D_diff-sorp_NA_NA_Unet-PF-20.pt |
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Notes: |
application/octet-stream |
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2DCFD_Unet.tar |
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Text: |
2D compressible NS eq pretrained model for Unet |
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Notes: |
application/x-tar |
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Label: |
2D_diff-react_NA_NA_Unet-1-step.pt |
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Notes: |
application/octet-stream |
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Label: |
2D_diff-react_NA_NA_Unet-AR.pt |
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Notes: |
application/octet-stream |
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2D_diff-react_NA_NA_Unet-PF-20.pt |
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Notes: |
application/octet-stream |
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2D_rdb_NA_NA_Unet-1-step.pt |
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Notes: |
application/octet-stream |
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Label: |
2D_rdb_NA_NA_Unet-AR.pt |
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Notes: |
application/octet-stream |
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Label: |
2D_rdb_NA_NA_Unet-PF-20.pt |
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Notes: |
application/octet-stream |
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Label: |
3DCFD_Unet.tar |
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Text: |
3D compressible NS eq pretrained model for Unet |
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Notes: |
application/x-tar |
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Label: |
advection_Unet-1.tar |
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Notes: |
text/plain |
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advection_Unet.tar |
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Text: |
1D Advection eq pretrained model for Unet |
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Notes: |
application/x-tar |
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burgers_Unet-1.tar |
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text/plain |
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burgers_Unet.tar |
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Text: |
1D Burgers eq pretrained model for Unet |
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Notes: |
application/x-tar |
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DarcyFlow_Unet.tar |
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Text: |
2D DarcyFlow pretrained model for Unet |
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Notes: |
application/x-tar |