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Data-driven science and engineering ...
~
Brunton, Steven L. (1984-)
Data-driven science and engineering :machine learning, dynamical systems, and control /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Data-driven science and engineering :Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.
其他題名:
machine learning, dynamical systems, and control /
作者:
Brunton, Steven L.
其他作者:
Kutz, Jose Nathan,
面頁冊數:
xxii, 472 pages :some color illustrations ;27 cm
標題:
EngineeringData processing.
ISBN:
9781108422093$q(hardcover ;
Data-driven science and engineering :machine learning, dynamical systems, and control /
Brunton, Steven L.1984-
Data-driven science and engineering :
machine learning, dynamical systems, and control /Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington. - xxii, 472 pages :some color illustrations ;27 cm
Includes bibliographical references and index.
"Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art"--
ISBN: 9781108422093$q(hardcover ;GBP$49.99
Standard No.: 40029110218
LCCN: 2018029888
Nat. Bib. No.: GBB914303bnb
Nat. Bib. Agency Control No.: 019220599UkSubjects--Topical Terms:
180008
Engineering
--Data processing.
LC Class. No.: TA330 / .B78 2019
Dewey Class. No.: 620.00285/631
Data-driven science and engineering :machine learning, dynamical systems, and control /
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Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.
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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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