Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

Projects without PN Affiliation

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151 to 160 of 375 Results

Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data Mar 14, 2024 - PN 6A-4 Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", https://doi.org/10.18419/DARUS-4087, DaRUS, V1 Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\ │ ├── plots\ ├── data_evaluation\ │ ├── data\ │ ├── notebooks\ │ ├── results\ │ ├── ut...
PN 2-7(Universität Stuttgart) Mar 13, 2024PN 2 SimTech Project PN 2-7 "Data-integrated training of surrogate models for uncertainty quantification and diagnostics of complex biological systems models"
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...

Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data

Mar 14, 2024 - PN 6A-4

Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", https://doi.org/10.18419/DARUS-4087, DaRUS, V1

Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\ │ ├── plots\ ├── data_evaluation\ │ ├── data\ │ ├── notebooks\ │ ├── results\ │ ├── ut...

PN 2-7(Universität Stuttgart)

Mar 13, 2024PN 2

SimTech Project PN 2-7 "Data-integrated training of surrogate models for uncertainty quantification and diagnostics of complex biological systems models"

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...

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