The goal of this project is to formulate and conduct benchmarks which assist in the validation of several computational models developed within the proposed CRC. Here, the main challenge arises from the possibly large uncertainties that are present in the experimental data as well as in the simulation results. A so-called validation metric which compares system response quantities of an experiment with the ones from a computational model has to integrate these uncertainties in a rigorous way. In the proposed project, such validation metrics will be developed by means of a Bayesian validation framework that incorporates parameter and conceptual uncertainty.
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1 to 6 of 6 Results
Model for: "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis"
Oct 13, 2025
Kohlhaas, Rebecca; Hommel, Johannes; Weinhardt, Felix; Class, Holger; Oladyshkin, Sergey; Flemisch, Bernd, 2025, "Model for: "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis"", https://doi.org/10.18419/DARUS-4653, DaRUS, V1
This dataset contains the code for the extended REV-scale EICP model for "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis". The model is available in two variation and can be run on three different initial states. The setup of these is described in the Readme Rel...
Surrogate model for: "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis"
Oct 13, 2025
Kohlhaas, Rebecca; Hommel, Johannes; Weinhardt, Felix; Class, Holger; Oladyshkin, Sergey; Flemisch, Bernd, 2025, "Surrogate model for: "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis"", https://doi.org/10.18419/DARUS-4654, DaRUS, V1
Evaluations and scripts for replication of "Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation supported by Bayesian Model Analysis". This includes model evaluations, visualization, surrogate training and evaluation and Bayesian model analysis.
BayesValidRox 2.1.0
Oct 1, 2025
Kohlhaas, Rebecca; Morales Oreamuno, Maria Fernanda; Lacheim, Alina, 2025, "BayesValidRox 2.1.0", https://doi.org/10.18419/DARUS-5323, DaRUS, V1
BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. Release 2.1.0 streamlines the sequential training of metamodels, and updates the metamodel validation and postprocessing functionalities.
The 11th SPE Comparative Solution Project: Submitted Data
Mar 22, 2025
Flemisch, Bernd; Nordbotten, Jan Martin; Fernø, Martin; Lie, Knut-Andreas; Kovscek, Anthony; Both, Jakub; Møyner, Olav; Sandve, Tor Harald; Di, Chaojie; Chen, Zhangxing; Gasanzade, Firdovsi; Bauer, Sebastian; Green, Christopher; Sayyafzadeh, Mohammad; Ghomian, Yousef; Ruby, Nicolas; Hadjisotiriou, George; Voskov, Denis; Franc, Jacques; Kachuma, Dickson; DING, Didier Yu; Flauraud, Eric; Youssef, Abdallah A.; Salinas, Pablo; Orsini, Paolo; Landa-Marbán, David; Lye, Kjetil Olsen; Torben, Jakob; Ahusborde, Etienne; Kern, Michel; Nole, Michael; Hammond, Glenn; Solovsky, Jakub; Firoozabadi, Abbas; Giddins, Marie Ann; Wendel, Kai; Koch, Timo; Class, Holger; Turner, Adam; Huang, Hai; Element, David; Fernandes, Bruno Ramon Batista; Krishnamurthy, Prasanna, 2025, "The 11th SPE Comparative Solution Project: Submitted Data", https://doi.org/10.18419/DARUS-4750, DaRUS, V1
Data submitted to the 11th Society of Petroleum Engineers Comparative Solution Project. Contains the sparse and dense data files of 77 results submitted by 18 participating groups for three cases SPE11A-C. Each zip file contains one such result, where the name speX_NAMEY.zip indicates Result Y for Case SPE11X of Participant NAME. Unpacking a result...
BayesValidRox 2.0.0
Mar 4, 2025
Kohlhaas, Rebecca; Morales Oreamuno, Maria Fernanda; Lacheim, Alina, 2025, "BayesValidRox 2.0.0", https://doi.org/10.18419/DARUS-4752, DaRUS, V1
BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. Release 2.0.0 improves the modularity and uniformity of the package and introduces template classes for surrogate models and methods for generating posterior samples.(2025-02-05)
BayesValidRox 1.1.0
Dec 13, 2024
Kohlhaas, Rebecca; Morales Oreamuno, Maria Fernanda, 2024, "BayesValidRox 1.1.0", https://doi.org/10.18419/DARUS-4613, DaRUS, V1
Release 1.1.0 of BayesValidRox. BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. (2024-07-18)
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