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License: CC BY 4.0

1 to 7 of 7 Results

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 Hel...
Experimental Data and Models for "Knowledge-Based Modeling of Simulation Behavior for Bayesian Optimization" Jul 1, 2023 - PN 5-1 Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, Miriam, 2023, "Experimental Data and Models for "Knowledge-Based Modeling of Simulation Behavior for Bayesian Optimization"", https://doi.org/10.18419/darus-3550, DaRUS, V1, UNF:6:QWTNrKTVYgetwfCBQfeWvw== [fileUNF] These files contain the data for the OpenDiHu experiments in sections 5.1.3 and 5.2. The settings used for the OpenDiHu simulations are in the opendihu/ folder. The simulation data is in the data/ folder and the used stan models are in the models/ folder. For more details see REA...
Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Feb 15, 2023 Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1 This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...
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...
Test Setup of Turbine Blade Data Mapping Feb 14, 2022 - Usability and Sustainability of Simulation Software Simonis, Frédéric; Davis, Kyle; Uekermann, Benjamin, 2022, "Test Setup of Turbine Blade Data Mapping", https://doi.org/10.18419/darus-2491, DaRUS, V1, UNF:6:9SbHetN1EMzpQrXWO5FEeA== [fileUNF] Input data, scripts, and results of the data mapping tests of section 2.2 of "Chourdakis et al., preCICE v2 - A Sustainable and User-Friendly Coupling Library, 2022".
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System Jul 21, 2020 - PINN Dynamic System Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1 This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods.
Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System Jul 21, 2020 - PINN Dynamic System Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1 This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne...

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 Hel...

Experimental Data and Models for "Knowledge-Based Modeling of Simulation Behavior for Bayesian Optimization"

Jul 1, 2023 - PN 5-1

Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, Miriam, 2023, "Experimental Data and Models for "Knowledge-Based Modeling of Simulation Behavior for Bayesian Optimization"", https://doi.org/10.18419/darus-3550, DaRUS, V1, UNF:6:QWTNrKTVYgetwfCBQfeWvw== [fileUNF]

These files contain the data for the OpenDiHu experiments in sections 5.1.3 and 5.2. The settings used for the OpenDiHu simulations are in the opendihu/ folder. The simulation data is in the data/ folder and the used stan models are in the models/ folder. For more details see REA...

Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Feb 15, 2023

Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1

This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...

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...

Test Setup of Turbine Blade Data Mapping

Feb 14, 2022 - Usability and Sustainability of Simulation Software

Simonis, Frédéric; Davis, Kyle; Uekermann, Benjamin, 2022, "Test Setup of Turbine Blade Data Mapping", https://doi.org/10.18419/darus-2491, DaRUS, V1, UNF:6:9SbHetN1EMzpQrXWO5FEeA== [fileUNF]

Input data, scripts, and results of the data mapping tests of section 2.2 of "Chourdakis et al., preCICE v2 - A Sustainable and User-Friendly Coupling Library, 2022".

Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

Jul 21, 2020 - PINN Dynamic System

Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1

This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods.

Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System

Jul 21, 2020 - PINN Dynamic System

Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1

This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne...

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