Data-driven science and engineering : 77 Steven L. Brunton and J. Nathan Kutz.

By:

Brunton, Steven L

Contributor(s):

Kutz, Jose Nathan

Material type: Text

TextPublication details: Cambridge : Cambridge University Press, 2019Description: 472 pages color illustrations ; 26 cmISBN:

978-1-108-42209-3

Subject(s):

SCIENCE--DATA PROCESSING

LOC classification:

GC TA 330 .B78 2019 c.1

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.

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Item type	Current library	Home library	Collection	Shelving location	Call number	Status	Date due	Barcode
Books	NU BALIWAG	NU BALIWAG	Computer Engineering	General Circulation	GC TA 330 .B78 2019 c.1 (Browse shelf(Opens below))	Available		NUBUL000003962

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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.

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