Machine Learning, Dynamical Systems and Control

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Steven L. Brunton is Associate Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Associate Professor of Applied Mathematics and a Data-Science Fellow at the eScience Institute. His research applies data science and machine learning for dynamical systems and control to fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He has co-authored three textbooks, received the Army and Air Force Young Investigator Program (YIP) awards and the Army Early Career in Science and Engineering (ECASE), and was awarded the University of Washington College of Engineering teaching award.

WEB: faculty.washington.edu/sbrunton/

 

ALSO BY THE AUTHOR

 

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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.

WEB: dmdbook.com

 

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This textbook discusses how to use machine learning to design nonlinear controllers for turbulence and other complex nonlinear systems. In particular, genetic programming is used to generate advanced control laws by breeding and mutating generations of candidate control laws.

WEB: Springer
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