Data-driven science and engineering [2nd ed.] : machine learning, dynamical systems, and control
By: Brunton, Steven L.
Contributor(s): Kutz, J. Nathan.
Publisher: Cambridge Cambridge University Press 2022Edition: 2nd ed.Description: xxiv, 590p.ISBN: 9781009098489.Subject(s): Engineering -- Data processing | Science -- Data processingDDC classification: 620.00285 | B838d2 Summary: "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"| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Books
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PK Kelkar Library, IIT Kanpur | General Stacks | 620.00285 B838d2 (Browse shelf) | Checked out to MALGATTE HARSH SIDDHARTH (S24061100) | 17/02/2025 | A186749 |
"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"
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