Stuttgart Center for Simulation Science (SC SimTech)

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61 to 70 of 293 Results

Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures Apr 18, 2024 - PN 1-X Keim, Leon; Class, Holger, 2024, "Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/DARUS-4089, DaRUS, V1 This dataset consists of software code associated with the publication titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes a Dockerimage that contains the precompiled code for immediate use. For transparency, the Dockerfile is also provided. 1 Download...
Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials Apr 11, 2024 - Holm group Finkbeiner, Jan; Tovey, Samuel; Holm, Christian, 2024, "Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials", https://doi.org/10.18419/DARUS-4099, DaRUS, V1 Data and scripts for replicating results and the investigation presented in the paper. This includes the dft parameters for generating training data, all training and data selection scripts for the neural networks, scripts for running and analysing the production simulations with the trained potentials.
Models and Prepared Datasets for the Second Stage Apr 2, 2024 - Surrogate models for groundwater flow simulations Pelzer, Julia, 2024, "Models and Prepared Datasets for the Second Stage", https://doi.org/10.18419/DARUS-3689, DaRUS, V1 Models trained with Heat Plume Prediction and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438. Based on raw data from doi:darus-3651 and doi:darus-3652.
Models and Prepared Datasets for the First Stage Apr 2, 2024 - Surrogate models for groundwater flow simulations Pelzer, Julia, 2024, "Models and Prepared Datasets for the First Stage", https://doi.org/10.18419/DARUS-3690, DaRUS, V1 Models trained with Heat Plume Prediction and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438. Based on raw data from doi:10.18419/darus-3649 and doi:10.18419/darus-3650.
Simulation Results from the Descriptive Graph-based Model of two Example Driving Scenarios Mar 28, 2024 - PN 7-6 Bechler, Florian, 2024, "Simulation Results from the Descriptive Graph-based Model of two Example Driving Scenarios", https://doi.org/10.18419/DARUS-4116, DaRUS, V1 Video of the interior and exterior information of a driving scenario, with the resulting graph-based description. The video shows a combination of exterior data of a driving scenario in combination with information about the drivers eyegaze. On the right side a resulting graph-based model is depicted which combines the states that are relevant for...
Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories Mar 27, 2024 - Publication Tools Roy, Sarbani; Wang, Fangfang; Gläser, Dennis, 2024, "Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories", https://doi.org/10.18419/DARUS-3785, DaRUS, V1 Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata from each of the files by applying corresponding parsers and then compile...
Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling Mar 25, 2024 - A01: Molecular detail in ﬂuid simulations: Density Functional Theory within component and momentum balances Bursik, Benjamin; Stierle, Rolf; Schlaich, Alexander; Rehner, Philipp; Gross, Joachim, 2024, "Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling", https://doi.org/10.18419/DARUS-3769, DaRUS, V1 This data set contains data of three categories: 1) LAMMPS input files (.lammps), postprocessing python script (.py) and density and velocity profiles (.dat) from NEMD. 2) DFT three-dimensional density profiles (.npy) for all systems. 3) Jupyter notebooks (.ipynb) for the calculation of densities from DFT, viscosity and velocity profiles from entro...
Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference Mar 14, 2024 - PN 2-7 Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/DARUS-4093, DaRUS, V1 This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.
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\...
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"

Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures

Apr 18, 2024 - PN 1-X

Keim, Leon; Class, Holger, 2024, "Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/DARUS-4089, DaRUS, V1

This dataset consists of software code associated with the publication titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes a Dockerimage that contains the precompiled code for immediate use. For transparency, the Dockerfile is also provided. 1 Download...

Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials

Apr 11, 2024 - Holm group

Finkbeiner, Jan; Tovey, Samuel; Holm, Christian, 2024, "Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials", https://doi.org/10.18419/DARUS-4099, DaRUS, V1

Data and scripts for replicating results and the investigation presented in the paper. This includes the dft parameters for generating training data, all training and data selection scripts for the neural networks, scripts for running and analysing the production simulations with the trained potentials.

Models and Prepared Datasets for the Second Stage

Apr 2, 2024 - Surrogate models for groundwater flow simulations

Pelzer, Julia, 2024, "Models and Prepared Datasets for the Second Stage", https://doi.org/10.18419/DARUS-3689, DaRUS, V1

Models trained with Heat Plume Prediction and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438. Based on raw data from doi:darus-3651 and doi:darus-3652.

Models and Prepared Datasets for the First Stage

Apr 2, 2024 - Surrogate models for groundwater flow simulations

Pelzer, Julia, 2024, "Models and Prepared Datasets for the First Stage", https://doi.org/10.18419/DARUS-3690, DaRUS, V1

Simulation Results from the Descriptive Graph-based Model of two Example Driving Scenarios

Mar 28, 2024 - PN 7-6

Bechler, Florian, 2024, "Simulation Results from the Descriptive Graph-based Model of two Example Driving Scenarios", https://doi.org/10.18419/DARUS-4116, DaRUS, V1

Video of the interior and exterior information of a driving scenario, with the resulting graph-based description. The video shows a combination of exterior data of a driving scenario in combination with information about the drivers eyegaze. On the right side a resulting graph-based model is depicted which combines the states that are relevant for...

Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories

Mar 27, 2024 - Publication Tools

Roy, Sarbani; Wang, Fangfang; Gläser, Dennis, 2024, "Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories", https://doi.org/10.18419/DARUS-3785, DaRUS, V1

Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata from each of the files by applying corresponding parsers and then compile...

Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling

Mar 25, 2024 - A01: Molecular detail in ﬂuid simulations: Density Functional Theory within component and momentum balances

Bursik, Benjamin; Stierle, Rolf; Schlaich, Alexander; Rehner, Philipp; Gross, Joachim, 2024, "Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling", https://doi.org/10.18419/DARUS-3769, DaRUS, V1

This data set contains data of three categories: 1) LAMMPS input files (.lammps), postprocessing python script (.py) and density and velocity profiles (.dat) from NEMD. 2) DFT three-dimensional density profiles (.npy) for all systems. 3) Jupyter notebooks (.ipynb) for the calculation of densities from DFT, viscosity and velocity profiles from entro...

Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Mar 14, 2024 - PN 2-7

Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/DARUS-4093, DaRUS, V1

This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.

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\...

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"

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