We develop physics-based and data-driven computational models as well as corresponding surrogate models for complex, continuous processes, for example for solid-liquid phase-change, processes in contact films or shallow flows.
Model-based development and decision support require an in-depth understanding of the predictive quality of the models used. To control this, we for instance use Bayesian methods for parameter estimation and model selection as well as probabilistic simulations.
Modeling of a complete system requires the description of its geometry, functionality and relevant processes, as well as its interaction with the ambient environment. We are working on integrating the silo solutions available for the individual levels.
We use innovative concepts of model-based development in order to increase the performance of intelligent, autonomous exploration robotics for space and polar applications and to better take into account the influence of extreme environmental conditions.
We develop model-based decision support systems including model development and digital infrastructure in order to facilitate the planing and implementation of engineered solutions against the effects of climate change.