Model reduction and approximation : theory and algorithms / edited by Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, Albert Cohen, Université Pierre at Marie Curie, Paris, France, Mario Ohlberger, Universität Münster, Münster, Germany, Karen Willcox, Massachusetts Institute of Technology, Cambridge, Massachusetts

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Computational science & engineering ; 15Verlag: Philadelphia : SIAM, Society for Industrial and Applied Mathematics, [2017], [2017]Beschreibung: 1 Online-RessourceISBN:

9781611974829

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Andere physische Formen: 9781611974812 | Erscheint auch als: Model reduction and approximation. Druck-Ausgabe. Philadelphia : Siam, Society for Industrial and Applied Mathematics, 2017. xx, 412 SeitenMSC: MSC: *65-06 | 65D05 | 65K10 | 00B15RVK: RVK: SK 905LOC-Klassifikation:

QA401

DOI: DOI: 10.1137/1.9781611974829Online-Ressourcen:

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Zusammenfassung: 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).PPN: PPN: 895849577Package identifier: Produktsigel: ZDB-72-SIA

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