Stuttgart Center for Simulation Science (SC SimTech)

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Publication Year: 2022

1 to 10 of 45 Results

PDEBench Datasets 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...
PDEBench Pretrained Models 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...
EHTM Code and Manual Oct 9, 2023 - Extended Hill-Type Muscle Material Model (EHTM) Nölle, Lennart; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V2 This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...
Interface Preserving Moving Mesh (Code) Mar 8, 2023 - Projects without PN Affiliation Alkämper, Maria; Magiera, Jim M., 2022, "Interface Preserving Moving Mesh (Code)", https://doi.org/10.18419/darus-1671, DaRUS, V2 Open source implementation in C++ for an interface preserving moving mesh in 2d and 3d using CGAL Delaunay triangulations. The time-dependent computational mesh allows for large point deformations while preserving a lower dimensional interface surface. See README.md for more info...
MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks Dec 16, 2022 - demoa Wochner, Isabell; Schmitt, Syn, 2022, "MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks", https://doi.org/10.18419/darus-3268, DaRUS, V1 This code allows you reproduce the optimal control and model predictive control results of the paper: "Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks" by Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt an...
demoa-base: a biophysics simulator for muscle-driven motion Nov 30, 2022 - demoa Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/darus-2550, DaRUS, V4 For more information, such as installation, requirements and user guide, please see the demoa-manual.pdf
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network 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...
Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps Oct 26, 2022 - Projects without PN Affiliation Davis, Kyle; Schulte, Miriam, 2022, "Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps", https://doi.org/10.18419/darus-3184, DaRUS, V1 This dataset provides all python3 code necessary to train convolutional neural networks built with pyTorch, as well as the data to train the networks. A README file is provided with instructions for accessing the training data, viewing the alread computed models, and all software...
COM-4 model to replicate simulation results for the GEO.KW project Oct 26, 2022 - Projects without PN Affiliation Davis, Kyle; Schulte, Miriam, 2022, "COM-4 model to replicate simulation results for the GEO.KW project", https://doi.org/10.18419/darus-3185, DaRUS, V1 This data repository contains the COM-4 model for the GEO.KW project. The dataset contains the PFLOTRAN and urbs simulation setup, spack build environment and spack build mirror to replicate the software build environment. A README file is provided, explaining the spack building...
REG-30 model to replicate simulation results for the GEO.KW project Oct 26, 2022 - Projects without PN Affiliation Davis, Kyle; Schulte, Miriam, 2022, "REG-30 model to replicate simulation results for the GEO.KW project", https://doi.org/10.18419/darus-3195, DaRUS, V1 This data repository contains the REG-30 model for the GEO.KW project. The dataset contains the PFLOTRAN and urbs software and simulation setup. Instructions for building the spack environment and running the simulation are provided in the README.

PDEBench Datasets

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...

PDEBench Pretrained Models

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...

EHTM Code and Manual

Oct 9, 2023 - Extended Hill-Type Muscle Material Model (EHTM)

Nölle, Lennart; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V2

This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...

Interface Preserving Moving Mesh (Code)

Mar 8, 2023 - Projects without PN Affiliation

Alkämper, Maria; Magiera, Jim M., 2022, "Interface Preserving Moving Mesh (Code)", https://doi.org/10.18419/darus-1671, DaRUS, V2

Open source implementation in C++ for an interface preserving moving mesh in 2d and 3d using CGAL Delaunay triangulations. The time-dependent computational mesh allows for large point deformations while preserving a lower dimensional interface surface. See README.md for more info...

MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

Dec 16, 2022 - demoa

Wochner, Isabell; Schmitt, Syn, 2022, "MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks", https://doi.org/10.18419/darus-3268, DaRUS, V1

This code allows you reproduce the optimal control and model predictive control results of the paper: "Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks" by Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt an...

demoa-base: a biophysics simulator for muscle-driven motion

Nov 30, 2022 - demoa

Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/darus-2550, DaRUS, V4

For more information, such as installation, requirements and user guide, please see the demoa-manual.pdf

Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network

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...

Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps

Oct 26, 2022 - Projects without PN Affiliation

Davis, Kyle; Schulte, Miriam, 2022, "Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps", https://doi.org/10.18419/darus-3184, DaRUS, V1

This dataset provides all python3 code necessary to train convolutional neural networks built with pyTorch, as well as the data to train the networks. A README file is provided with instructions for accessing the training data, viewing the alread computed models, and all software...

COM-4 model to replicate simulation results for the GEO.KW project

Oct 26, 2022 - Projects without PN Affiliation

Davis, Kyle; Schulte, Miriam, 2022, "COM-4 model to replicate simulation results for the GEO.KW project", https://doi.org/10.18419/darus-3185, DaRUS, V1

This data repository contains the COM-4 model for the GEO.KW project. The dataset contains the PFLOTRAN and urbs simulation setup, spack build environment and spack build mirror to replicate the software build environment. A README file is provided, explaining the spack building...

REG-30 model to replicate simulation results for the GEO.KW project

Oct 26, 2022 - Projects without PN Affiliation

Davis, Kyle; Schulte, Miriam, 2022, "REG-30 model to replicate simulation results for the GEO.KW project", https://doi.org/10.18419/darus-3195, DaRUS, V1

This data repository contains the REG-30 model for the GEO.KW project. The dataset contains the PFLOTRAN and urbs software and simulation setup. Instructions for building the spack environment and running the simulation are provided in the README.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications