Statistical Model-Data Integration

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.This enables new insights into the merits of process-based vs. data-driven approaches, and provides a rigorous foundation for hybrid model building and evaluation. Our mission is to set new standards in data-integrated simulation which are expected to enable innovations of substantial societal impact.

We advance methods for uncertainty quantification, model evaluation and diagnostics, model error detection, model improvement and surrogate modeling. Our main application field is in Earth Sciences (e.g., soil-plant modeling in the context of agriculture and biodiversity, surface hydrology for flood risk assessment, groundwater flow and contaminant transport, preferential flow and gas migration in porous media), 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!