Stuttgart Center for Simulation Science (SC SimTech)

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Subject: Computer and Information Science Subject: Earth and Environmental Sciences

1 to 10 of 30 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.
Surrogate models for groundwater flow simulations(Universität Stuttgart) Jul 28, 2023Projects without PN Affiliation
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.
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: 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...
Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures $Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures$ 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 immedia...
Raw Simulation Datasets for Extending Heat Plumes Apr 9, 2024 - Surrogate models for groundwater flow simulations Pelzer, Julia, 2024, "Raw Simulation Datasets for Extending Heat Plumes", https://doi.org/10.18419/darus-4133, DaRUS, V1 These data sets serve as training and testing data for modelling the extension of temperature field emanating from one groundwater heat pump. There are simulated with Pflotran and saved in h5 format. The data set for training is called "dataset_medium_k_3e-10_1000dp". It contains...
Projects without PN Affiliation(Universität Stuttgart) Mar 1, 2021 This dataverse hosts data(-verses) without a project network affiliation.
PN 5-6(Universität Stuttgart) Feb 20, 2020PN 5 SimTech Project PN 5-6 "Physics-informed ANNs for dynamic, distributed and stochastic systems"
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"

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.

Surrogate models for groundwater flow simulations(Universität Stuttgart)

Jul 28, 2023Projects without PN Affiliation

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.

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: 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...

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

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

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 immedia...

Raw Simulation Datasets for Extending Heat Plumes

Apr 9, 2024 - Surrogate models for groundwater flow simulations

Pelzer, Julia, 2024, "Raw Simulation Datasets for Extending Heat Plumes", https://doi.org/10.18419/darus-4133, DaRUS, V1

These data sets serve as training and testing data for modelling the extension of temperature field emanating from one groundwater heat pump. There are simulated with Pflotran and saved in h5 format. The data set for training is called "dataset_medium_k_3e-10_1000dp". It contains...

Projects without PN Affiliation(Universität Stuttgart)

Mar 1, 2021

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

PN 5-6(Universität Stuttgart)

Feb 20, 2020PN 5

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

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"

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