Stuttgart Center for Simulation Science (SC SimTech)

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Subject: Earth and Environmental Sciences

1 to 10 of 42 Results

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.
tBME project(Universität Stuttgart) Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3) tBME project
Surrogate models for groundwater flow simulations(Universität Stuttgart) Jul 28, 2023Projects without PN 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.
SciML PDE Benchmark(Universität Stuttgart) Mar 23, 2022Projects without PN Affiliation This is a benchmark suite of PDE simulations comprising data for testing the performance of novel simulation or emulation methods against classical simulations.
Sampling Strategies of the Regime-and-memory model (RMM) Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty Gonzalez-Nicolas Alvarez, Ana, 2021, "Sampling Strategies of the Regime-and-memory model (RMM)", https://doi.org/10.18419/darus-2035, DaRUS, V1, UNF:6:JeAvfovoq369qtbASSmQjg== [fileUNF] This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling strategies. River discharge frequency sampling strategies. Low Q sampling strategies. High Q sampling strategies. L...
Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence Oct 26, 2022 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/darus-2815, DaRUS, V1 This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion perc...
Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures $Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures$ Apr 18, 2024 - PN 1-X Keim, Leon; Class, Holger, 2024, "Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/darus-4143, DaRUS, V1 This dataset features both data and code related to the research article titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes raw data packaged in tarball format, including Python scri...
Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/darus-1776, DaRUS, V1 This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data a...
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...

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.

tBME project(Universität Stuttgart)

Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

tBME project

Surrogate models for groundwater flow simulations(Universität Stuttgart)

Jul 28, 2023Projects without PN 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.

SciML PDE Benchmark(Universität Stuttgart)

Mar 23, 2022Projects without PN Affiliation

This is a benchmark suite of PDE simulations comprising data for testing the performance of novel simulation or emulation methods against classical simulations.

Sampling Strategies of the Regime-and-memory model (RMM)

Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty

Gonzalez-Nicolas Alvarez, Ana, 2021, "Sampling Strategies of the Regime-and-memory model (RMM)", https://doi.org/10.18419/darus-2035, DaRUS, V1, UNF:6:JeAvfovoq369qtbASSmQjg== [fileUNF]

This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling strategies. River discharge frequency sampling strategies. Low Q sampling strategies. High Q sampling strategies. L...

Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence

Oct 26, 2022 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/darus-2815, DaRUS, V1

This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion perc...

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

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

Apr 18, 2024 - PN 1-X

Keim, Leon; Class, Holger, 2024, "Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/darus-4143, DaRUS, V1

This dataset features both data and code related to the research article titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes raw data packaged in tarball format, including Python scri...

Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data

Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/darus-1776, DaRUS, V1

This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data a...

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...

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