Stuttgart Center for Simulation Science (SC SimTech)

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Subject: Earth and Environmental Sciences

1 to 10 of 42 Results

EXC 2075 Project Networks(Universität Stuttgart) Sep 6, 2019
PN 5(Universität Stuttgart) Sep 6, 2019EXC 2075 Project Networks SimTech EXC 2075 Project Network 5 "On-the-fly model modification, error control, and simulation adaptivity"
PN 1(Universität Stuttgart) Sep 6, 2019EXC 2075 Project Networks SimTech EXC 2075 Project Network 1 "Data-integrated models and methods for multiphase fluid dynamics"
PN 5-6(Universität Stuttgart) Feb 20, 2020PN 5 SimTech Project PN 5-6 "Physics-informed ANNs for dynamic, distributed and stochastic systems"
PINN Dynamic System(Universität Stuttgart) Feb 20, 2020PN 5-6 This dataverse contains dataset and codes for the submitted publication: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W. (2020): Physics-inspired Artificial Neural Network structure improves prediction: Application to a Thermochemical Energy Storage System
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...
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.
Projects without PN Affiliation(Universität Stuttgart) Mar 1, 2021 This dataverse hosts data(-verses) without a project network affiliation.
Stochastic Simulation and Safety Research for Hydrosystems (LS3)(Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart) Mar 12, 2021Projects without PN Affiliation This dataverse contains data associated with the projects (as sub-dataverses) of LS3.
Modeling Strategies for Gas migration in Subsurface (IWS, Universität Stuttgart) Mar 12, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3) This dataverse contains modeling data (percolation type) and comparison methods (codes) for the project models for gas migration in porous media.

EXC 2075 Project Networks(Universität Stuttgart)

Sep 6, 2019

PN 5(Universität Stuttgart)

Sep 6, 2019EXC 2075 Project Networks

SimTech EXC 2075 Project Network 5 "On-the-fly model modification, error control, and simulation adaptivity"

PN 1(Universität Stuttgart)

Sep 6, 2019EXC 2075 Project Networks

SimTech EXC 2075 Project Network 1 "Data-integrated models and methods for multiphase fluid dynamics"

PN 5-6(Universität Stuttgart)

Feb 20, 2020PN 5

SimTech Project PN 5-6 "Physics-informed ANNs for dynamic, distributed and stochastic systems"

PINN Dynamic System(Universität Stuttgart)

Feb 20, 2020PN 5-6

This dataverse contains dataset and codes for the submitted publication: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W. (2020): Physics-inspired Artificial Neural Network structure improves prediction: Application to a Thermochemical Energy Storage System

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...

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.

Projects without PN Affiliation(Universität Stuttgart)

Mar 1, 2021

This dataverse hosts data(-verses) without a project network affiliation.

Stochastic Simulation and Safety Research for Hydrosystems (LS3)(Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart)

Mar 12, 2021Projects without PN Affiliation

This dataverse contains data associated with the projects (as sub-dataverses) of LS3.

Modeling Strategies for Gas migration in Subsurface (IWS, Universität Stuttgart)

Mar 12, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

This dataverse contains modeling data (percolation type) and comparison methods (codes) for the project models for gas migration in porous media.

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