Interpolatory Methods for Model Reduction - pr_1749532

Interpolatory Methods for Model Reduction

By Athanasios C. Antoulas

Paperback

$228.50

Or 4 payments of $57.12 with

delivery message Free delivery for orders over $49.99

Add to Wish List
Delivered in 10 - 14 days
Available for Click and Collect
Dynamical systems are a principal tool in the modeling, prediction, and control of a wide range of complex phenomena. As the need for improved accuracy leads to larger and more complex dynamical systems, direct simulation often becomes the only available strategy for accurate prediction or control, inevitably creating a considerable burden on computational resources. This is the main context where one considers model reduction, seeking 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. Interpolatory methods are among the most widely used model reduction techniques, and Interpolatory Methods for Model Reduction is the first comprehensive analysis of this approach available in a single, extensive resource. It introduces state-of-the-art methods reflecting significant developments over the past two decades, covering both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks. This textbook is appropriate for a wide audience of engineers and other scientists working in the general areas of large-scale dynamical systems and data-driven modeling of dynamics.

Product code: 9781611976076

ISBN 9781611976076
Publisher Society for Industrial & Applied Mathematics,U.S.
No. Of Pages 232
Series Computational Science and Engineering 21
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.