The overarching goal of the working group is the development of a holistic approach for dynamic image reconstruction and information extraction which enables efficient treatment of high-dimensional dynamic inverse problems. This is achieved through the development of both model-based and data-driven approaches, followed by techniques to derive a computationally feasible approximation. The group's subjects are both at the forefront of research in inverse problems and crucial for a wide range of future technological developments currently out of reach.

The three research areas modelling, learning, and applications build the three pillars of the working group. The key ingredient is an intensive cooperation between the three project areas with experts from inverse problems, machine learning, optimization and variational methods, numerical analysis and model reduction, as well as engineering and life science applications; this holistic approach is entirely novel in the research field and opens the door to a methodology and applications whose innovation power can barely be foreseen.