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: Text

TextPublisher: Cambridge : Cambridge University Press, 2019Description: xxii, 472 pages : illustrations : 26 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781108422093

Subject(s):

DDC classification:

620.00285/631 23

LOC classification:

TA330 .B78 2019

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.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Books	Raman Research Institute Library	681.32.062 BRU (Browse shelf(Opens below))	Available		29341

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