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Dynamic Mode Decomposition

Dynamic Mode Decomposition
Data-Driven Modeling of Complex Systems

£59.99

  • Date Published: January 2017
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9781611974492

£ 59.99
Paperback

This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
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About the Authors
  • Data-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® 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.

    • Highlights the numerous innovations around the DMD algorithm
    • Blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses
    • Provides data for intuitive examples of key methods, extensive MATLAB® code, and graphical presentations
    Read more

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    Product details

    • Date Published: January 2017
    • format: Paperback
    • isbn: 9781611974492
    • length: 248 pages
    • dimensions: 255 x 177 x 16 mm
    • weight: 0.55kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    Notation
    Acronyms
    1. Dynamic mode decomposition: an introduction
    2. Fluid dynamics
    3. Koopman analysis
    4. Video processing
    5. Multiresolution DMD
    6. DMD with control
    7. Delay coordinates, ERA, and hidden Markov models
    8. Noise and power
    9. Sparsity and DMD
    10. DMD on nonlinear observables
    11. Epidemiology
    12. Neuroscience
    13. Financial trading
    Glossary
    Bibliography
    Index.

  • Authors

    J. Nathan Kutz, University of Washington
    J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics, Adjunct Professor of Physics and Electrical Engineering, and Senior Data Science Fellow with the eScience Institute at the University of Washington.

    Steven L. Brunton, University of Washington
    Steven L. Brunton is an Assistant Professor of Mechanical Engineering, Adjunct Assistant Professor of Applied Mathematics, and a Data Science Fellow with the eScience Institute at the University of Washington.

    Bingni W. Brunton, University of Washington
    Bingni W. Brunton is the Washington Research Foundation Innovation Assistant Professor of Biology and a Data Science Fellow with the eScience Institute at the University of Washington.

    Joshua L. Proctor, Institute for Disease Modeling
    Joshua L. Proctor is an Associate Principal Investigator with the Institute for Disease Modeling, Washington, as well as Affiliate Assistant Professor of Applied Mathematics and Mechanical Engineering at the University of Washington.

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