Speaker
Description
Almost all areas in the physical or engineering sciences rely on computational models to some extent. The models can be based on fundamental physics processes (physics-based) which typically leads to a set of differential equations. Alternatively, machine learning techniques can be used to infer input-output relations out of very large sets of data. Both approaches come with different strengths and weaknesses but they rely on mathematical algorithms to function reliably and efficiently. In the last couple of years, we are also increasingly seeing synergies between both worlds, for example when ML is used as part of a numerical algorithm for solving the differential equations of a physics-based model.
Our poster will present various case studies where our mathematical research helped to improve models. Applications include fusion reactor modeling, simulations of combustion engines, in-silico modeling of osteoarthritis, and medical imaging.
