Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

Projects without PN Affiliation

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71 to 80 of 293 Results

PN 1-X(Universität Stuttgart) Mar 12, 2024PN 1
PN 6A-3(Universität Stuttgart) Mar 8, 2024PN 6 Advanced learning strategies for potential energy surfaces applied to organic electrolytes
PN 3A-7(Universität Stuttgart) Mar 8, 2024PN 3 Advanced Learning Strategies for Machine Learned Interatomic Potentials
PN 6A-4(Universität Stuttgart) Mar 8, 2024PN 6 Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory
Replication Data for: Development of a framework for point-particle tracking Mar 7, 2024 - Usability and Sustainability of Simulation Software Kschidock, Helena, 2024, "Replication Data for: Development of a framework for point-particle tracking", https://doi.org/10.18419/DARUS-3760, DaRUS, V1 This dataset contains a custom build for Altair's simulation software ultraFluidX and the input files used in the Master Thesis 'Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows', submitted by Helena Kschidock on 02/11/2023. For further details on the input files, a...
Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/DARUS-4031, DaRUS, V1 This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the steps to reproduce the results in the readme.md file.
Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries(Universität Stuttgart) Feb 22, 2024Projects without PN Affiliation The AMI is a limb amputation technique which aims to maintain the mechanical and neural feedback between agonist and antagonist muscle. We aim to develop a platform for in silico analysis which can provide insight to surgeons. For that we combine detailed multi-physics multi-scale simulations with surrogate models for optimization.
Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes Feb 16, 2024 - PN3-5 Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1 The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta...
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 dataset is intended to progress the scientific ML research area. In ge...
Replication Data for: Constraint-aware neural networks for Riemann problems Jan 11, 2024 - Projects without PN Affiliation Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/DARUS-3869, DaRUS, V1 Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in the README.md.

PN 1-X(Universität Stuttgart)

Mar 12, 2024PN 1

PN 6A-3(Universität Stuttgart)

Mar 8, 2024PN 6

Advanced learning strategies for potential energy surfaces applied to organic electrolytes

PN 3A-7(Universität Stuttgart)

Mar 8, 2024PN 3

Advanced Learning Strategies for Machine Learned Interatomic Potentials

PN 6A-4(Universität Stuttgart)

Mar 8, 2024PN 6

Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory

Replication Data for: Development of a framework for point-particle tracking

Mar 7, 2024 - Usability and Sustainability of Simulation Software

Kschidock, Helena, 2024, "Replication Data for: Development of a framework for point-particle tracking", https://doi.org/10.18419/DARUS-3760, DaRUS, V1

This dataset contains a custom build for Altair's simulation software ultraFluidX and the input files used in the Master Thesis 'Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows', submitted by Helena Kschidock on 02/11/2023. For further details on the input files, a...

Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models

Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries

Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/DARUS-4031, DaRUS, V1

This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the steps to reproduce the results in the readme.md file.

Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries(Universität Stuttgart)

Feb 22, 2024Projects without PN Affiliation

The AMI is a limb amputation technique which aims to maintain the mechanical and neural feedback between agonist and antagonist muscle. We aim to develop a platform for in silico analysis which can provide insight to surgeons. For that we combine detailed multi-physics multi-scale simulations with surrogate models for optimization.

Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes

Feb 16, 2024 - PN3-5

Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1

The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta...

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 dataset is intended to progress the scientific ML research area. In ge...

Replication Data for: Constraint-aware neural networks for Riemann problems

Jan 11, 2024 - Projects without PN Affiliation

Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/DARUS-3869, DaRUS, V1

Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in the README.md.

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