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.

�ber die Autorinnen und Autoren

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 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 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 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.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.