11 to 20 of 21 Results
May 2, 2023 - DuMux
Oukili, Hamza; Ackermann, Sina; Buntic, Ivan; Class, Holger; Coltman, Edward; Flemisch, Bernd; Ghosh, Tufan; Gläser, Dennis; Grüninger, Christoph; Hommel, Johannes; Jupe, Tim; Keim, Leon; Kelm, Mathis; Kiemle, Stefanie; Koch, Timo; Kostelecky, Anna Mareike; Pallam, Harsha Vardhan; Schneider, Martin; Stadler, Leopold; Utz, Martin; Wang, Yue; Wendel, Kai; Winter, Roman; Wu, Hanchuan, 2023, "DuMux 3.7.0", https://doi.org/10.18419/DARUS-3405, DaRUS, V1
Release 3.7.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE. |
Feb 2, 2023 - SusI Final Workshop
Gläser, Dennis; Seeland, Anett; Schulze, Katharina; Burbulla, Samuel, 2023, "Verification benchmarks for single-phase flow in three-dimensional fractured porous media: DuMuX source code", https://doi.org/10.18419/DARUS-3228, DaRUS, V1
This dataset contains the source code for simulating the benchmark cases of Berre et al. (2021) with the open-source simulator DuMuX. The benchmarks focus on flow and transport through fractured porous media, considering fracture networks of varying complexity. The code in this dataset can be used, for instance, to reproduce the results published a... |
Jan 26, 2023 - DuMux
Kelm, Mathis; Ackermann, Sina; Buntic, Ivan; Coltman, Edward; Flemisch, Bernd; Gläser, Dennis; Grüninger, Christoph; Heck, Katharina; Hommel, Johannes; Keim, Leon; Kiemle, Stefanie; Koch, Timo; Lipp, Melanie; Schneider, Martin; Schollenberger, Theresa; Stadler, Leopold; Utz, Martin; Veyskarami, Maziar; Wang, Yue; Wendel, Kai; Werner, David; Wu, Hanchuan, 2023, "DuMux 3.6.0", https://doi.org/10.18419/DARUS-3247, DaRUS, V1
Release 3.6.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE. |
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... |
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... |
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.... |
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. |
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... |
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 and post-processing codes for the manuscript can be found here. |
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. |
