Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
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ID: 287864
2019
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Ranked #1,053 of 1,244 articles by views in ADVANCES IN ARCHAEOLOGICAL PRACTICE
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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.
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persistent_1761420003_68fd22e33eded
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| Authors | J. Nathan Kutz |
| Journal | ADVANCES IN ARCHAEOLOGICAL PRACTICE |
| Year | 2019 |
| DOI |
10.1017/9781108380690
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| URL | |
| Keywords | Keywords not found |
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