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

Data-Driven Science and Engineering 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 engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art. Examples using MATLAB are provided throughout the book, with some examples also including Deep Learning Toolbox. In addition, the book's companion website contains videos illustrating examples solved in MATLAB.

  • Provides in-depth examples paired with comprehensive code
  • Features concise, digestible explanations of complex concepts and their applications
  • Includes extensive online supplements with homework, case studies, and supplementary code

About This Book

Steven L. Brunton, University of Washington
J. Nathan Kutz, University of Washington

Cambridge University Press, 2019

ISBN: 9781108422093
Language: English

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