PDEBench Pretrained Models

Version 2.0
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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Subtitle Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning"
Description

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.

Subject Computer and Information Science; Earth and Environmental Sciences; Physics
Keyword Benchmark, Scientific Machine Learning, Physics-Informed Machine Learning, Machine Learning, Partial Differential Equation
Related Publication 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.
Notes This version includes the pretrained model weights trained using the latest Advection and Burgers equation data.
License/Data Use Agreement

CC BY 4.0

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1D_Advection_Sols_beta4.0_FNO.pt
1D/Advection/Train/Unknown - 518.6 KB
Published Jun 21, 2022
89 Downloads
MD5: 38479648059adc6d77bf7c60911ddc35
Advection pre-trained model (FNO) (1D)
Data
1D_Advection_Sols_beta4.0_Unet.pt
1D/Advection/Train/Unknown - 31.1 MB
Published Jun 21, 2022
145 Downloads
MD5: e37c31313eefd5568c11af0c36075834
Advection pre-trained model (Unet) (1D)
Data
1D_Burgers_Sols_Nu1.0_FNO.pt
1D/Burgers/Train/Unknown - 518.6 KB
Published Jun 21, 2022
98 Downloads
MD5: 09510743c315a505c42e2da4c5313a9d
Burgers pre-trained model (FNO) (1D)
Data
1D_Burgers_Sols_Nu1.0_Unet.pt
1D/Burgers/Train/Unknown - 31.1 MB
Published Jun 21, 2022
96 Downloads
MD5: fedc814f213e17be7338e493c6cffabe
Burgers pre-trained model (Unet) (1D)
Data
1D_CFD_Shock_trans_Train_FNO.pt
1D/CFD/Train/Unknown - 522.1 KB
Published Jun 21, 2022
60 Downloads
MD5: 341dc4818086d1bd2275aee63460d832
CFD pre-trained model (FNO) (1D)
Data
1D_CFD_Shock_trans_Train_Unet.pt
1D/CFD/Train/Unknown - 31.1 MB
Published Jun 21, 2022
62 Downloads
MD5: 8a6b749897d882e0a9acefcb1ec98c36
CFD pre-trained model (Unet) (1D)
Data
ReacDiff_Nu1.0_Rho2.0_FNO.pt
1D/ReactionDiffusion/Train/Unknown - 518.6 KB
Published Jun 21, 2022
58 Downloads
MD5: 1082c465d6fd2b20434bbf26fc247572
ReactionDiffusion pre-trained model (FNO) (1D)
Data
ReacDiff_Nu1.0_Rho2.0_Unet.pt
1D/ReactionDiffusion/Train/Unknown - 31.1 MB
Published Jun 21, 2022
49 Downloads
MD5: a4e3aa1dd921312791ddf17fa2718a07
ReactionDiffusion pre-trained model (Unet) (1D)
Data
1DCFD_FNO.tar
fno/TAR Archive - 2.6 MB
Published Jun 21, 2022
82 Downloads
MD5: f0ad9bb3bc212fd905038a2cfcb5b190
1D compressible NS eq pretrained model for FNO
Data
1DReacDiff_FNO.tar
fno/TAR Archive - 2.0 MB
Published Jun 21, 2022
74 Downloads
MD5: 5f40110c251c5f16aa94ca324bdc5630
1D Reaction-Diffusion eq pretrained model for FNO
Data
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