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.
Publications in Internal Project D03
- Oladyshkin, S., Mohammadi, F., Kroeker, I., & Nowak, W. (2020). Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory. Entropy, 22(8), 890. https://doi.org/10.3390/e22080890
- Jaust, A., Weishaupt, K., Mehl, M., & Flemisch, B. (2020). Partitioned Coupling Schemes for Free-Flow and Porous-Media Applications with Sharp Interfaces. In R. Klöfkorn, E. Keilegavlen, F. A. Radu, & J. Fuhrmann (Eds.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (pp. 605--613). Springer International Publishing.
- Oladyshkin, S., & Nowak, W. (2019). The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design. Entropy, 21(11), 1081. https://doi.org/10.3390/e21111081
- Schneider, M., Gläser, D., Flemisch, B., & Helmig, R. (2018). Comparison of finite-volume schemes for diffusion problems. Oil & Gas Science and Technology – Revue d’IFP Energies Nouvelles, 73, 82. https://doi.org/10.2516/ogst/2018064