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Product Identifiers
PublisherSociety for Industrial AND Applied Mathematics
ISBN-101611974496
ISBN-139781611974492
eBay Product ID (ePID)227645900
Product Key Features
Number of Pages250 Pages
LanguageEnglish
Publication NameDynamic Mode Decomposition : Data-Driven Modeling of Complex Systems
SubjectMechanics / Fluids, Probability & Statistics / General, Mathematical Analysis
Publication Year2016
TypeTextbook
AuthorJ. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor
Subject AreaMathematics, Science
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight19.5 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2016-024120
Dewey Edition23
IllustratedYes
Dewey Decimal518/.2
Table Of ContentPreface Notations Acronyms Chapter 1: Dynamic Mode Decomposition: An Introduction Chapter 2: Fluid Dynamics Chapter 3: Koopman Analysis Chapter 4: Video Processing Chapter 5: Multiresolution DMD Chapter 6: DMD with Control Chapter 7: Delay Coordinates, ERA, and Hidden Markov Models Chapter 8: Noise and Power Chapter 9: Sparsity and DMD Chapter 10: DMD on Nonlinear Observables Chapter 11: Epidemiology Chapter 12: Neuroscience Chapter 13: Financial Trading Glossary Bibliography Index.
SynopsisData-driven dynamical systems is a burgeoning field, connecting how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is the first book to address the DMD algorithm and present a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. By blending theoretical development, example codes, and applications, the theory and its many innovations and uses are showcased. The efficacy of the DMD algorithm is shown through the inclusion of example problems from engineering, physical sciences, and biological sciences, and the authors provide extensive MATLAB(R) code, data for intuitive examples of key methods, and graphical presentations. This book can therefore be used in courses that integrate data analysis with dynamical systems, and will be a useful resource for engineers and applied mathematicians., The first book to address the DMD algorithm presenting a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. It blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses., Data-driven dynamical systems is a burgeoning field - it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition is the first book to address the DMD algorithm, it: Presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. Blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses. Highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences. Provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
LC Classification NumberQA402.2.D96 2016
