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

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control - 1st Edition


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

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.

Books Detail

  • Title: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
  • Author: Steven L. Brunton, J. Nathan Kutz
  • Length: 492 pages
  • Edition: 1
  • Language: English
  • Publisher: Cambridge University Press
  • Publication Date: April 18, 2019
  • ISBN-10: 1108422098
  • ISBN-13: 978-1108422093
  • Sales Rank:  #122,341 (See Top 100 Books)

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GIFTCOURSE: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.
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