Interpolatory methods are among the most widely used model reduction techniques. This book is the first comprehensive analysis of this approach available in a single, extensive resource. It covers both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks.

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Bibliographic Details

Title: Interpolatory Methods for Model Reduction
Publisher: Society for Industrial and Applied Mathematics
Publication Date: 2020
Language: English
ISBN 10: 1611976073
ISBN 13: 9781611976076
Binding: Paperback
Condition: Very Good

About this title

Synopsis

Interpolatory methods are among the most widely used model reduction techniques, used to replace large systems of coupled differential and algebraic equations that constitute high fidelity system models with substantially fewer equations that are crafted to control the loss of fidelity that order reduction may induce in the system response. This textbook is the first comprehensive analysis of this approach available in a single, extensive resource. It introduces state-of-the-art methods and covers both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks.

About the Author

Athanasios C. Antoulas is a professor in the Department of Electrical and Computing Engineering at Rice University. He is a fellow of the Max-Planck Society, a fellow of the IEEE, and an adjunct professor of molecular and cellular biology at the Baylor College of Medicine. Christopher Beattie is a professor in the Department of Mathematics and in the Division of Computational Modeling and Data Analytics at Virginia Tech. Serkan Gugercin is the Class of 1950 Professor of Mathematics, deputy director of the Division of Computational Modeling and Data Analytics, and an affiliated faculty in the Department of Mechanical Engineering at Virginia Tech.

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