J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics and Electrical and Computer Engineering at the University of Washington, and served as department chair of Applied Mathematics from 2007— 2015. He is also Adjunct Professor of Mechanical Engineering and Physics and a Senior Data-Science Fellow at the eScience institute. His research interests are in complex systems and data analysis where machine learning can be integrated with dynamical systems and control for a diverse set of applications. He has received the NSF CAREER award, an Applied Mathematics Boeing Award of Excellence in Teaching and the Best Paper Award at the International Conference of Applied and Engineering Mathematics in 2009.

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Highlights the emerging methods of machine learning and AI applied towards the engineering and physical sciences. Focus on data-driven dynamical systems and control.

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This book focuses on scientific computing methods related to solving differential equations, boundary value problems, and partial differential equations. It also introduces data-driven methods for aiding in these methods.

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The first textbook to give an in-depth treatment of the emerging data-driven method of dynamic mode decomposition. Extensive theory, applications and codes are provided.

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