Stability Preservation For Parametric Model Order Reduction By Matrix Interpolation

Stability Preservation for Parametric Model Order Reduction by Matrix Interpolation

In dieser Arbeit wird das Problem der Stabilitätserhaltung für parametrische Modellreduktion mittels Matrixinterpolation untersucht. Hierfür werden die benötigten mathematischen Grundlagen aus der Systemtheorie eingeführt. Es werden darüber hinaus die beiden bekanntesten Modellreduktionsverfahren für lineare Systeme betrachtet und ein kurzer Überblick über verschiedene relevante Methoden zur parametrischen Modellreduktion gegeben. Die titelgebende Matrixinterpolation wird im Detail analysiert, und es werden die verschiedenen Schwierigkeiten des Verfahrens, als auch existierende Lösungen aus der Literatur, untersucht. Auf diesen aufbauend wird ein Verfahren zur Erweiterung von lokalen Unterräumen vorgeschlagen, während für die aus der Literatur bekannten Verfahren zur Stabilitätserhaltung mögliche Probleme aufgezeigt und neue theoretische Resultate gegeben werden. Es wird als Alternative ein neuartiges, flexibles Verfahren zur Stabilitätserhaltung vorgeschlagen, dessen potenzielle Vor- und Nachteile für zwei numerische Beispiele gezeigt werden. In this thesis the problem of stability preservation for parametric model order reduction by matrix interpolation is investigated. For this purpose the necessary mathematical fundamentals from system theory are given. Furthermore the two most popular model order reduction methods for linear systems are looked at and a brief introduction to various relevant methods for parametric model order reduction is given. The title giving matrix interpolation is analyzed in detail and its various problems, as well as solutions from literature, are studied. Based on these a procedure for the extension of local subspaces is given, whereas for the stability preservation methods known from literature possible problems are shown and new theoretical results are given. As an alternative a novel, flexible method for stability preservation is proposed and its potential pros and cons are shown for two numerical examples.

Stability Preservation for Parametric Model Order Reduction by Matrix Interpolation

Model Reduction and Approximation

Many physical, chemical, biomedical, and technical processes can be described by partial differential equations or dynamical systems. In spite of increasing computational capacities, many problems are of such high complexity that they are solvable only with severe simplifications, and the design of efficient numerical schemes remains a central research challenge. This book presents a tutorial introduction to recent developments in mathematical methods for model reduction and approximation of complex systems. Model Reduction and Approximation: Theory and Algorithms contains three parts that cover (I) sampling-based methods, such as the reduced basis method and proper orthogonal decomposition, (II) approximation of high-dimensional problems by low-rank tensor techniques, and (III) system-theoretic methods, such as balanced truncation, interpolatory methods, and the Loewner framework. It is tutorial in nature, giving an accessible introduction to state-of-the-art model reduction and approximation methods. It also covers a wide range of methods drawn from typically distinct communities (sampling based, tensor based, system-theoretic).?? This book is intended for researchers interested in model reduction and approximation, particularly graduate students and young researchers.