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Data-driven science and engineering : machine learning, dynamical systems, and control / 2nd ed

Data-driven science and engineering : machine learning, dynamical systems, and control / 2nd ed (1회 대출)

자료유형
단행본
개인저자
Brunton, Steven L. (Steven Lee), 1984- author. Kutz, Jose Nathan, author.
서명 / 저자사항
Data-driven science and engineering : machine learning, dynamical systems, and control / Steven L. Brunton, J. Nathan Kutz.
판사항
2nd ed.
발행사항
Cambridge :   Cambridge University Press,   2022.  
형태사항
xxiv, 590 p. : ill. (some col.) ; 26 cm.
ISBN
9781009098489
요약
"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"--
서지주기
Includes bibliographical references and index.
일반주제명
Engineering --Data processing. Science --Data processing.
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020 ▼a 9781009098489 ▼q (hardback)
020 ▼z 9781009089517 ▼q (epub)
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090 ▼a 620.00285631 ▼b B911d2
100 1 ▼a Brunton, Steven L. ▼q (Steven Lee), ▼d 1984- ▼e author.
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 : ▼b Cambridge University Press, ▼c 2022.
264 1 ▼a Cambridge, United Kingdom, New York, NY : ▼b Cambridge University Press, ▼c 2022.
300 ▼a xxiv, 590 p. : ▼b ill. (some col.) ; ▼c 26 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
504 ▼a Includes bibliographical references and index.
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.
650 0 ▼a Science ▼x Data processing.
700 1 ▼a Kutz, Jose Nathan, ▼e author.
945 ▼a ITMT

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 620.00285631 B911d2 등록번호 111869887 도서상태 대출중 반납예정일 2023-04-11 예약 예약가능 R 서비스 M

컨텐츠정보

목차

Part I. Dimensionality Reduction and Transforms: 1. Singular Value Decomposition; 2. Fourier and Wavelet Transforms; 3. Sparsity and Compressed Sensing; Part II. Machine Learning and Data Analysis: 4. Regression and Model Selection; 5. Clustering and Classification; 6. Neural Networks and Deep Learning; Part III. Dynamics and Control: 7. Data-Driven Dynamical Systems; 8. Linear Control Theory; 9. Balanced Models for Control; Part IV. Advanced Data-Driven Modeling and Control: 10. Data-Driven Control; 11. Reinforcement Learning; 12. Reduced Order Models (ROMs); 13. Interpolation for Parametric ROMs; 14. Physics-Informed Machine Learning.

관련분야 신착자료

Doolan, Con J (2022)
Sharma, Atul, (Professor of mechanical engineering) (2022)