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

Brunton, Steven L.; J. Nathan Kutz

ISBN 10: 1108422098 / ISBN 13: 9781108422093

Published by Cambridge University Press, 2019

About this Item

Very good, clean, tight condition. Text free of marks. No marks to cover except for small paint flake off an edge of the spine. Professional book dealer since 1999. All orders are processed promptly and carefully packaged. Seller Inventory # 086045

Bibliographic Details

Title: Data-Driven Science and Engineering: Machine...

Publisher: Cambridge University Press

Publication Date: 2019

Binding: Hardcover

Condition: Very Good

Edition: 1st Edition

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.