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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, J. Nathan Kutz.
其他題名:	machine learning, dynamical systems, and control /
作者:	Brunton, Steven L.
其他作者:	Kutz, Jose Nathan,
出版者:	Cambridge, United Kingdom ;Cambridge University Press,2022.
面頁冊數:	xxiv, 590 p. :ill. (some col.) ;26 cm.
標題:	EngineeringData processing.
ISBN:	9781009098489 :

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, J. Nathan Kutz. - 2nd ed. - Cambridge, United Kingdom ;Cambridge University Press,2022. - xxiv, 590 p. :ill. (some col.) ;26 cm.

Includes bibliographical references and index.

Table of contents: Dimensionality reduction and transforms; Singular value decomposition (SVD); Fourier and wavelet transforms; Sparsity and compressed sensing; Machine learning and data analysis; Regression and model selection; Clustering and classification; Neural networks and deep learning; Dynamics and control; Data-driven systems; Linear control theory; Balanced models for control; Advanced data-driven modeling and control; Data-driven control; Reinforcement learning; Reduced-order models (ROMs); Interpolation for parametric reduced-order models; Physics-informed machine learning.

"Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material including lecture videos per section, homeworks, data, and codes in MATLAB, Python, and Julia available on databookuw.com"--

ISBN: 9781009098489 :NT$1949

LCCN: 2022000124

Nat. Bib. No.: GBC230770bnb

Nat. Bib. Agency Control No.: 020494895UkSubjects--Topical Terms:

180008
Engineering
--Data processing.

LC Class. No.: TA330 / .B78 2022

Dewey Class. No.: 620.00285

Data-driven science and engineering :machine learning, dynamical systems, and control /
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250 $a 2nd ed.
260 $a Cambridge, United Kingdom ; $a New York, NY : $b Cambridge University Press, $c 2022.
300 $a xxiv, 590 p. : $b ill. (some col.) ; $c 26 cm.
504 $a Includes bibliographical references and index.
505 0 $a Table of contents: Dimensionality reduction and transforms; Singular value decomposition (SVD); Fourier and wavelet transforms; Sparsity and compressed sensing; Machine learning and data analysis; Regression and model selection; Clustering and classification; Neural networks and deep learning; Dynamics and control; Data-driven systems; Linear control theory; Balanced models for control; Advanced data-driven modeling and control; Data-driven control; Reinforcement learning; Reduced-order models (ROMs); Interpolation for parametric reduced-order models; Physics-informed machine learning.
520 $a "Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material including lecture videos per section, homeworks, data, and codes in MATLAB, Python, and Julia available on databookuw.com"-- $c Provided by publisher.
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650 0 $a Science $x Data processing. $3 180009
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