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

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ID: 287864
2019
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Abstract
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.
Reference Key
persistent_1761420003_68fd22e33eded Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J. Nathan Kutz
Journal ADVANCES IN ARCHAEOLOGICAL PRACTICE
Year 2019
DOI
10.1017/9781108380690
URL
Keywords Keywords not found

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