Data-driven science and engineering : machine learning, dynamical systems, and control / Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.
Material type: TextPublisher: Cambridge : Cambridge University Press, ©2019Description: xxv, 472 p. : ill. ; 26 cmISBN:- 9781108422093 (hardback : alk. paper)
- 23 620.00285631 BRD
Item type | Current library | Call number | Copy number | Status | Date due | Barcode |
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Books | Central Library, SUST General Stacks | 620.00285631 BRD (Browse shelf(Opens below)) | 1 | Available | 0076438 |
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620.0015194 GUN Numerical methods for engineers / | 620.00182 HAS Statistical models in engineering / | 620.0028553 DUA Advanced engineering mathematics with MATLAB / | 620.00285631 BRD Data-driven science and engineering : | 620.0042 ARD Desing of Structures Considering Creep / | 620.0042 BAF Fundamentals of tool engineering design / | 620.0042 BAF Fundamentals of tool engineering design / |
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"-- Provided by publisher.
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