Machine Learning, Dynamical Systems and Control

Stacks Image 6
J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington, and served as department chair from 2007— 2015. He is also Adjunct Professor of Electrical 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.




Stacks Image 17
An excellent pre-cursor to the current book, 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.

WEB: Book Site


Stacks Image 22
The first textbook to give an in-depth treatment of the emerging data-driven method of the dynamic mode decomposition. Extensive theory, applications and codes are provided.