  The reduced-order modeling of a system from data (also known as system
identification) is a classical task in system and control theory and well
understood for standard linear systems with the so-called Loewner framework as
one of many established approaches. In the case of descriptor systems for which
the transfer function is not proper anymore, recent research efforts have
addressed strategies to deal with the non-proper parts more or less explicitly.
In this work, we propose a variant of a Loewner matrix-based interpolation
algorithm that implicitly addresses possibly non-proper components of the
system response. We evaluate the performance of the suggested approach by
comparing against recently-developed explicit algorithms for which we propose a
linearized Navier-Stokes model with a significant non-proper behavior as a
benchmark example.
