Stuttgart Center for Simulation Science (SC SimTech)

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231 to 240 of 300 Results

Code of the tBME method Nov 24, 2021 - tBME project Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1 Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....
Code for relative permeabilities for two-phase flow between parallel plates with slip conditions Nov 24, 2021 - PN 1-6 Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/DARUS-2241, DaRUS, V1 This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_relativepermeabilities.pdf", where also the resulting equations are li...
tBME project(Universität Stuttgart) Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3) tBME project
PN 1-6(Universität Stuttgart) Nov 22, 2021PN 1 SimTech Project PN 1-6 "Upscaling of two-phase porous media flow based on fluid morphology."
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 - PN 6 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/DARUS-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
PN 6-6(Universität Stuttgart) Sep 29, 2021PN 6 SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"
preCICE Distribution Version v2104.0 Sep 27, 2021 - Usability and Sustainability of Simulation Software Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/DARUS-2125, DaRUS, V1 The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archive this specific version v2104.0 of the distribution. If you want to u...
Usability and Sustainability of Simulation Software(Universität Stuttgart) Sep 21, 2021Institute for Parallel and Distributed Systems
Visual Analytics System for Hidden States in Recurrent Neural Networks Sep 10, 2021 - PN 6-4 Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/DARUS-2052, DaRUS, V1 Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analysis system. The sub directories contain the following: dataPreparation...

Code of the tBME method

Nov 24, 2021 - tBME project

Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1

Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....

Code for relative permeabilities for two-phase flow between parallel plates with slip conditions

Nov 24, 2021 - PN 1-6

Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/DARUS-2241, DaRUS, V1

This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_relativepermeabilities.pdf", where also the resulting equations are li...

tBME project(Universität Stuttgart)

Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

tBME project

PN 1-6(Universität Stuttgart)

Nov 22, 2021PN 1

SimTech Project PN 1-6 "Upscaling of two-phase porous media flow based on fluid morphology."

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021 - PN 6

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/DARUS-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

PN 6-6(Universität Stuttgart)

Sep 29, 2021PN 6

SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

preCICE Distribution Version v2104.0

Sep 27, 2021 - Usability and Sustainability of Simulation Software

Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/DARUS-2125, DaRUS, V1

The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archive this specific version v2104.0 of the distribution. If you want to u...

Usability and Sustainability of Simulation Software(Universität Stuttgart)

Sep 21, 2021Institute for Parallel and Distributed Systems

Visual Analytics System for Hidden States in Recurrent Neural Networks

Sep 10, 2021 - PN 6-4

Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/DARUS-2052, DaRUS, V1

Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analysis system. The sub directories contain the following: dataPreparation...

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