Abstract Dynamic Mode Decomposition (DMD) is a model-order reduction approach,
whereby spatial modes of fixed temporal frequencies are extracted from numerical or …

Abstract Model Order Reduction (MOR) techniques play a crucial role in reducing the
computational complexity of high-dimensional mathematical models, enabling efficient …

In this article, a new model order reduction scheme is proposed for the simplification of large-
scale linear dynamical models. The proposed technique is based on the balanced …

This paper presents a new data-driven non-intrusive reduced-order model (NIROM) that
outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder …

Considering the use of dynamical systems in practical applications, often only limited
regions in the time or frequency domain are of interest. Therefore, it usually pays off to …

In this thesis, structure-preserving model order reduction for dynamical systems is studied.
The particular focus lies on mechanical systems described by differential equations with …

This paper presents a stable reduced order approximation method of fractional-order
systems based on the grey wolf optimizer hybridized with cuckoo search algorithm (GWO …

We consider operator-valued differential Lyapunov and Riccati equations, where the
operators B and C may be relatively unbounded with respect to A (in the standard notation) …

In this paper, we present a finite-time model order reduction method for linear systems via
shifted Legendre polynomials. The main idea of the approach is to use finite-time empirical …

In this paper, we first investigate the finite-frequency model order reduction for linear
systems based on low-rank Gramian approximations. An efficient algorithm for computing …