In mid-November, Anneli Guthke, leader of the SimTech junior research group Statistical Model-Data Integration at the University of Stuttgart, was invited to give a talk at RWTH Aachen University. As part of the seminar series of the Chair of Methods for Model-based Development in Computational Engineering (MBD), she presented her research on Bayesian methods for handling uncertainty in Earth science modelling. The visit was hosted by Anil Yildiz and Julia Kowalski, whose group she joined for two days of scientific exchange and discussion.
In her talk, Anneli Guthke highlighted how Bayesian approaches can be used to rigorously quantify and reduce uncertainty in complex simulation models, for example, in hydrology, hydrogeology, and other areas of the geosciences and engineering. By systematically combining observational data with physics-based models, Bayesian model-data integration enables more transparent and robust predictions, while also making assumptions and remaining uncertainties explicit.
The visit was designed not only as a lecture but as a platform for methodological dialogue. In meetings and informal discussions with the MBD group, shared interests emerged around topics such as model evaluation, diagnosis of model deficiencies, and the design of robust, data-informed simulation workflows. “Visiting this methods-oriented Chair has been a truly enriching experience,” says Guthke, “as it shows the value of advancing statistical methods for diverse disciplines that share similar modelling challenges. I am looking forward to joint initiatives with RWTH Aachen to promote Bayesian model-data integration in engineering and the Geosciences.”
Her stay in Aachen also provided an excellent opportunity to reconnect with Florian Wellmann, with whom she shares a strong interest in information-theoretic tools for Earth science applications. Such tools allow researchers to compare and quantify the information content of different data sets and models, and to design experiments or measurement campaigns that are particularly informative for a given modelling goal.
Anneli Guthke’s junior research group at SimTech focuses on bringing together data-driven and physics-based modelling within a coherent statistical framework. Central themes include Bayesian uncertainty quantification and the use of information-theoretic measures to place models and data on a common information scale. The overarching aim is to develop new standards for assessing and improving simulation models that integrate heterogeneous data sources, with applications ranging from environmental and Earth system sciences to engineering.