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Data-driven science and engineering : machine learning, dynamical systems, and control / Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.

By: Brunton, Steven L. (Steven Lee), 1984- [author.].
Contributor(s): Kutz, Jose Nathan [author.].
Material type: materialTypeLabelBookPublisher: Cambridge : Cambridge University Press, 2019Description: pages cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781108422093 (hardback : alk. paper).Subject(s): Engineering -- Data processing | Science -- Data processing | Mathematical analysisDDC classification: 620.00285/631 Summary: "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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"-- Provided by publisher.
List(s) this item appears in: New Arrivals Dec 2019 | New arrivals January 2020
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62x (Browse shelf) Checked out 23/04/2020 AT-ISTA#001998
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Includes bibliographical references and index.

"Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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"-- Provided by publisher.

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