Stochastic Simulation and Safety Research for Hydrosystems (LS3)

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1 to 10 of 18 Results

Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Feb 23, 2026 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani; Guthke (geb. Schöniger), Anneli; Nowak, Wolfgang, 2026, "Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data", https://doi.org/10.18419/DARUS-3592, DaRUS, V1 This dataset contains the codes for the invasion percolation models and comparison method used in the manuscript: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data
Data for Tau method Feb 13, 2026 - tBME project Hsueh, Han-Fang, 2026, "Data for Tau method", https://doi.org/10.18419/DARUS-2936, DaRUS, V1 Code and data for the publication "Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions" (2024). The folder "code_tau_method" includes the files and data to generate the plots for cases of a didactic example, one synthetic dataset and one real dataset discussed in the publication. It includes seven 9 *.m files, 2 folders...
Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data Mar 25, 2024 - Geostatistical Inversion Xu, Teng, 2024, "Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data", https://doi.org/10.18419/DARUS-2382, DaRUS, V1, UNF:6:vW5NY5+W7Wy1GtNndUOs3Q== [fileUNF] Description: Dataset published with the paper "Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison". You can use these data to generate the comparisons between the EnKF and the MCMC solution as seen in the paper. Folder structu...
Geostatistical Inversion(Universität Stuttgart) Mar 19, 2024
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 the publication "Learning Groundwater Contaminant Diffusion-Sorption Pr...
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 percolation (SIP) models discussed in the manuscript; it can be used by th...
Code of the tBME method Nov 24, 2021 - tBME project Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1 Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....
tBME project(Universität Stuttgart) Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3) tBME project
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. Low and High Q sampling strategies.
Regime-and-memory model (RMM) Code Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty Gonzalez-Nicolas Alvarez, Ana, 2021, "Regime-and-memory model (RMM) Code", https://doi.org/10.18419/DARUS-2034, DaRUS, V1 We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with memory, using an autocorrelation over time.One explicit parameter of our model represents the export regime. This parameter can morph the model among chemostatic-type and chemodyn...

Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

Feb 23, 2026 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani; Guthke (geb. Schöniger), Anneli; Nowak, Wolfgang, 2026, "Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data", https://doi.org/10.18419/DARUS-3592, DaRUS, V1

This dataset contains the codes for the invasion percolation models and comparison method used in the manuscript: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

Data for Tau method

Feb 13, 2026 - tBME project

Hsueh, Han-Fang, 2026, "Data for Tau method", https://doi.org/10.18419/DARUS-2936, DaRUS, V1

Code and data for the publication "Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions" (2024). The folder "code_tau_method" includes the files and data to generate the plots for cases of a didactic example, one synthetic dataset and one real dataset discussed in the publication. It includes seven 9 *.m files, 2 folders...

Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data

Mar 25, 2024 - Geostatistical Inversion

Xu, Teng, 2024, "Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data", https://doi.org/10.18419/DARUS-2382, DaRUS, V1, UNF:6:vW5NY5+W7Wy1GtNndUOs3Q== [fileUNF]

Description: Dataset published with the paper "Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison". You can use these data to generate the comparisons between the EnKF and the MCMC solution as seen in the paper. Folder structu...

Geostatistical Inversion(Universität Stuttgart)

Mar 19, 2024

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 the publication "Learning Groundwater Contaminant Diffusion-Sorption Pr...

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 percolation (SIP) models discussed in the manuscript; it can be used by th...

Code of the tBME method

Nov 24, 2021 - tBME project

Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1

Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....

tBME project(Universität Stuttgart)

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

tBME project

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. Low and High Q sampling strategies.

Regime-and-memory model (RMM) Code

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

Gonzalez-Nicolas Alvarez, Ana, 2021, "Regime-and-memory model (RMM) Code", https://doi.org/10.18419/DARUS-2034, DaRUS, V1

We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with memory, using an autocorrelation over time.One explicit parameter of our model represents the export regime. This parameter can morph the model among chemostatic-type and chemodyn...

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