Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and
wide applicability for simulating large-scale mathematical models in engineering and the …

This paper introduces a new framework for constructing the discrete empirical interpolation
method (\sf DEIM) projection operator. The interpolation node selection procedure is …

Given the square matrices A,B,D,E and the matrix C of conforming dimensions, we consider
the linear matrix equation A\mathbfXE+D\mathbfXB=C in the unknown matrix \mathbfX. Our …

Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …

We discuss the relation of a certain type of generalized Lyapunov equations to Gramians of
stochastic and bilinear systems together with the corresponding energy functionals. While …

Large scale dynamical systems are a common framework for the modeling and control of
many complex phenomena of scientific interest and industrial value, with examples of …

In this paper, we will discuss the problem of optimal model order reduction of bilinear control
systems with respect to the generalization of the well-known \calH_2-norm for linear …

We introduce a data‐driven model order reduction approach that represents an extension of
the Loewner framework for linear and bilinear systems to the case of quadratic‐bilinear (QB) …

The Loewner framework for model reduction is extended to the class of bilinear systems.
The main advantage of this framework over existing ones is that the Loewner pencil …