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

Brunton, Steven L.; Kutz, J. Nathan

ISBN 10: 1009098489 / ISBN 13: 9781009098489

Published by Cambridge University Press, 2022

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Synopsis:

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

About the Authors: Steven L. Brunton is the James B. Morrison Professor of Mechanical Engineering at the University of Washington and Associate Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Applied Mathematics and Computer Science and a Data-Science Fellow at the eScience Institute. His research merges data science and machine learning with dynamical systems and control, with applications in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is an author of three textbooks, and received the UW College of Engineering Teaching award, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE) 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. He is an author of three textbooks and has received the Applied Mathematics Boeing Award of Excellence in Teaching and an NSF CAREER award.

Bibliographic Details

Title: Data-Driven Science and Engineering: Machine...
Publisher: Cambridge University Press
Publication Date: 2022
Binding: Hardcover
Condition: good
Edition: 2nd Edition