Conference Agenda

Dynamical systems are omnipresent in various physical processes. In view

of analysis, simulation and (predictive) control, state-space models are often of central

importance. To make these models accessible for computation, efficient discretization

techniques and complexity reduction, e.g., by model order reduction, are often inevitable.

If no state-space model is at hand, one can resort to data-based techniques to identify a

surrogate model from measurements.

The aim of this miniysymposium is to bring together young researchers working in

the broad field of dynamical systems and their approximation via discretization, model

reduction and data-based methods. In particular, we unite representatives and young

experts from different communities such as operator and semigroup theory, model-order

reduction, differential-algebraic equations, multi-body dynamics and optimization-based

control to spark discussions and cooperations across these disciplines.