  In this contribution, we discuss the modeling and model reduction framework
known as the Loewner framework. This is a data-driven approach, applicable to
large-scale systems, which was originally developed for applications to linear
time-invariant systems. In recent years, this method has been extended to a
number of additional more complex scenarios, including linear parametric or
nonlinear dynamical systems. We will provide here an overview of the latter
two, together with time-domain extensions. Additionally, the application of the
Loewner framework is illustrated by a collection of practical test cases.
Firstly, for data-driven complexity reduction of the underlying model, and
secondly, for dealing with control applications of complex systems (in
particular, with feedback controller design).
