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Data-driven science and engineering :machine learning, dynamical systems, and control /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-driven science and engineering :Steven L. Brunton, J. Nathan Kutz.
Reminder of title:	machine learning, dynamical systems, and control /
Author:	Brunton, Steven L.
other author:	Kutz, Jose Nathan,
Published:	Cambridge, United Kingdom ;Cambridge University Press,2022.
Description:	xxiv, 590 p. :ill. (some col.) ;26 cm.
Subject:	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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100 1 $a Brunton, Steven L. $q (Steven Lee), $d 1984- $e author. $3 857120
245 1 0 $a Data-driven science and engineering : $b machine learning, dynamical systems, and control / $c Steven L. Brunton, J. Nathan Kutz.
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.
650 0 $a Engineering $x Data processing. $3 180008
650 0 $a Science $x Data processing. $3 180009
650 0 $a Mathematical analysis. $3 186133
650 0 $a Machine learning. $3 188639
700 1 $a Kutz, Jose Nathan, $e author. $3 654035
776 0 8 $i Online version: $a Brunton, Steven L., (Steven Lee), 1984- $t Data-driven science and engineering $d Cambridge, United Kingdom, New York, NY : Cambridge University Press, 2022 $z 9781009089517 $w (DLC) 2022000125