Merging data-driven with physics-based modeling approaches, as targeted by the SimTech Cluster of Excellence, requires also merging on the level of statistical evaluation. Our group therefore aims to fuse Bayesian uncertainty assessment with information-theoretic measures to bring (lack of) information in physics-based models and information in data to the same scale. Our mission is to set new standards in data-integrated simulation model assessment which are expected to enable innovations of substantial societal impact.
We advance methods for uncertainty quantification, model evaluation and diagnostics, model error detection, and model improvement. Our favorite application fields include hydrological, hydrogeological, environmental, and biological systems, but the fundamental ideas of our methodological research allow for transfer to arbitrary disciplines.
Reach out to us if you are interested in joining our group as a student or researcher, or in collaborating on common interests!