Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Brunton, Steven/ Kutz, J. Nathan

ISBN 10: 1108422098 / ISBN 13: 9781108422093

Published by Cambridge Univ Pr, 2019

About this Item

472 pages. 10.25x7.25x1.00 inches. In Stock.

Seller Inventory # zk1108422098

Bibliographic Details

• Title: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
• Publisher: Cambridge Univ Pr
• Publication Date: 2019
• Language: English
• ISBN 10: 1108422098
• ISBN 13: 9781108422093
• Binding: Hardcover
• Condition: Brand New
• Weight: 1.19 kilograms

About this title

Synopsis

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

About the Author

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 two textbooks, received the Army and Air Force Young Investigator awards, and was awarded the University of Washington College of Education teaching award.
 
J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington and Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Electrical and Computer Engineering, Mechanical Engineering, and Physics and Senior Data-Science Fellow at the eScience Institute. His research interests lie at the intersection of dynamical systems and machine learning.