1 to 5 of 5 Results
Feb 13, 2024 - SciML PDE Benchmark
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
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... |
Nov 30, 2023 - SciML PDE Benchmark
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
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... |
Nov 2, 2022 - PN 5-6
Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/darus-3249, DaRUS, V1
This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in t... |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1
This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods. |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1
This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne... |