Stuttgart Center for Simulation Science (SC SimTech)

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README.md Dec 16, 2024 - Replication Data for: Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems Markdown Text - 9.8 KB - MD5: 86ea29d28cb567ff38ee537e7660ad21 Description of the data repository, software dependencies and installation instructions for preCICE and ASTE.
requirements.txt Dec 16, 2024 - Replication Data for: Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems Plain Text - 317 B - MD5: 19ec6243d4e8986aba83295a148557d7 Python packages used for the experiments.
PN 3A-9(Universität Stuttgart) Dec 13, 2024PN 3 Bottom-up modelling of COF/electrode systems for CO2 reduction in confinement
PN 1-4(Universität Stuttgart) Dec 11, 2024PN 1 SimTech Project PN 1-4 "Image-based morphological characterization of multiphase porous media flow"
Models and Prepared Datasets for 3D-CNN - First Stage Nov 26, 2024 - Surrogate models for groundwater flow simulations Trick, Johanna, 2024, "Models and Prepared Datasets for 3D-CNN - First Stage", https://doi.org/10.18419/DARUS-4534, DaRUS, V1 Models trained with Heat Plume Prediction 3D and datasets prepared with Heat Plume Prediction 3D into reasonable format, normalization used to train these models. Based on raw data from doi:darus-4533.
args.yaml Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage Unknown - 164 B - MD5: 04074ce881f8e0dd010c2b2c2c217078
args.yaml Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage Unknown - 165 B - MD5: ace3532bd852f4d3cbbad822fb9e9889
avg_error_xy_slice.png Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage PNG Image - 17.1 KB - MD5: 9219aac3bbf6162645ee43188f48ba28
avg_error_xy_slice.png Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage PNG Image - 18.3 KB - MD5: 912dda7c1a21c099e6efe6335f1e32c7
avg_error_xy_slice.png Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage PNG Image - 18.0 KB - MD5: eaee0cde51c65eb02d179efe3e071fb3

README.md

Dec 16, 2024 - Replication Data for: Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems

Markdown Text - 9.8 KB -

Description of the data repository, software dependencies and installation instructions for preCICE and ASTE.

requirements.txt

Dec 16, 2024 - Replication Data for: Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems

Plain Text - 317 B -

Python packages used for the experiments.

PN 3A-9(Universität Stuttgart)

Dec 13, 2024PN 3

Bottom-up modelling of COF/electrode systems for CO2 reduction in confinement

PN 1-4(Universität Stuttgart)

Dec 11, 2024PN 1

SimTech Project PN 1-4 "Image-based morphological characterization of multiphase porous media flow"

Models and Prepared Datasets for 3D-CNN - First Stage

Nov 26, 2024 - Surrogate models for groundwater flow simulations

Trick, Johanna, 2024, "Models and Prepared Datasets for 3D-CNN - First Stage", https://doi.org/10.18419/DARUS-4534, DaRUS, V1

Models trained with Heat Plume Prediction 3D and datasets prepared with Heat Plume Prediction 3D into reasonable format, normalization used to train these models. Based on raw data from doi:darus-4533.

args.yaml

Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage

Unknown - 164 B -

args.yaml

Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage

Unknown - 165 B -

avg_error_xy_slice.png

Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage

PNG Image - 17.1 KB -

avg_error_xy_slice.png

Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage

PNG Image - 18.3 KB -

avg_error_xy_slice.png

Nov 26, 2024 - Models and Prepared Datasets for 3D-CNN - First Stage

PNG Image - 18.0 KB -

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