Model Reduction and Approximation: Theory and Algorithms

Benner P, Cohen A, Ohlberger M, Willcox K

Book (monograph) | Peer reviewed

Abstract

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; is tutorial in nature, giving an accessible introduction to state-of-the-art model reduction and approximation methods; and covers a wide range of methods drawn from typically distinct communities (sampling based, tensor based, system-theoretic).

Details about the publication

Publishing companySIAM Publications
Place of publicationPhiladelphia, PA
Title of seriesComputational Science and Engineering
Volume of series15
StatusPublished
Release year2017
Language in which the publication is writtenEnglish
ISBN978-1-611974-81-2
DOI10.1137/1.9781611974829
Link to the full texthttps://doi.org/10.1137/1.9781611974829

Authors from the University of Münster

Ohlberger, Mario
Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger)
Center for Nonlinear Science
Center for Multiscale Theory and Computation