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.