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Dynamic Mode Decomposition

Dynamic Mode Decomposition

Dynamic Mode Decomposition

Data-Driven Modeling of Complex Systems
J. Nathan Kutz , University of Washington
Steven L. Brunton , University of Washington
Bingni W. Brunton , University of Washington
Joshua L. Proctor , Institute for Disease Modeling
December 2026
Not yet published - no date available
Paperback
9781611974492

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    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

    Product details

    December 2026
    Paperback
    9781611974492
    248 pages
    255 × 177 × 16 mm
    0.55kg
    Not yet published - no date available

    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.