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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : World of Books (was SecondSale), Montgomery, IL, Etats-Unis
Etat : Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00078233195
Quantité disponible : 4 disponible(s)
Vendeur : World of Books (was SecondSale), Montgomery, IL, Etats-Unis
Etat : Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00078288656
Quantité disponible : 1 disponible(s)
Vendeur : HPB-Red, Dallas, TX, Etats-Unis
paperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! N° de réf. du vendeur S_312874443
Quantité disponible : 1 disponible(s)
Vendeur : Basi6 International, Irving, TX, Etats-Unis
Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N° de réf. du vendeur ABEOCT25-182412
Quantité disponible : 1 disponible(s)
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Paperback. Etat : new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781611976076
Quantité disponible : 1 disponible(s)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781611976076_new
Quantité disponible : 2 disponible(s)
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur GB-9781611976076
Quantité disponible : 2 disponible(s)
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Paperback. Etat : New. N° de réf. du vendeur 6666-GRD-9781611976076
Quantité disponible : 2 disponible(s)
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : New. N° de réf. du vendeur 40435181-n
Quantité disponible : 1 disponible(s)
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 40435181
Quantité disponible : 1 disponible(s)