Baur, UlrikeBenner, PeterHaasdonk, BernardHimpe, ChristianMaier, ImmanuelOhlberger, MarioMax-Planck-Institut für Dynamik Komplexer Technischer Systeme2025-05-292015https://epflicht.bibliothek.uni-halle.de/handle/123456789/3940870914545urn:nbn:de:gbv:3:2-646772483843Abstract: Dynamical systems of large order appear in many applications. For an efficient simulation it can become necessary to reduce the system dimension using a reliable model order reduction method, in particular in a many-query context when the system is to be solved for varying parameters and input signals. Nowadays, it is often required that the models include physical parameters to allow more flexibility in simulation. These parameters should be preserved in the reduced-order system; a task that motivates the development of new approaches to model order reduction referred to collectively as parametric model order reduction. In this work, we compare several methods for parametric model order reduction using common benchmark problems from the literature.1 Online-Ressource (37 Seiten = 0,87 MB) : Diagrammeenghttp://rightsstatements.org/vocab/InC/1.0/510Book