One of the main approaches to model reduction of both linear and nonlinear dynamical
systems is by means of interpolation. This approach seeks reduced models whose transfer …

The AAA algorithm has become a popular tool for data-driven rational approximation of
single-variable functions, such as transfer functions of a linear dynamical system. In the …

In this paper, we present a novel method for approximating bilinear time-delay systems by
bilinear systems with no delay. This is performed by means of matching some input-output …

The Loewner framework is one of the most successful data-driven model order reduction
techniques. If N is the cardinality of a given data set, the so-called Loewner and shifted …

We propose a method for fitting quadratic-bilinear models from data. Although the dynamics
characterizing the original model consist of general analytic nonlinearities, we rely on lifting …

The problem of the fluid closure for the collisionless linear Vlasov system is investigated
using a perspective from control theory and model order reduction. The balanced truncation …

Modeling of dynamical systems is at the core of the simulation and controller design of
modern technological products and processes. Due to the ever-increasing demand for …

An ever‐increasing need for improving the accuracy includes more involved and detailed
features, thus inevitably leading to larger‐scale dynamical systems [1]. To overcome this …

The optimization and control of tokamak plasmas requires predicting the transport of matter
and heat in a way that is both efficient and accurate. Triggered by kinetic instabilities …

This paper presents an efficient macromodeling technique for modeling distributed circuits
characterized by noisy frequency-domain data. The proposed method is based on an …