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.
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

81 to 82 of 82 Results
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)
Dec 13, 2024 - BayesValidRox 1.1.0
Gzip Archive - 140.9 KB - MD5: 37c66a1cecb9867545a7dec06da1d711
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.