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: xxii, 472 pages : illustrations : 26 cmContent type: - text
- unmediated
- volume
- 9781108422093
- 620.00285/631 23
- TA330 .B78 2019
| Item type | Current library | Call number | Status | Date due | Barcode | |
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Raman Research Institute Library | 681.32.062 BRU (Browse shelf(Opens below)) | Available | 29341 |
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| 681.32.062 BER "Algorithms: The construction, proof, and analysis of programs" | 681.32.062 BRA Theory of computation | 681.32.062 BRA Algorithmics: Theory and practice | 681.32.062 BRU Data-driven science and engineering : | 681.32.062 BUR The hundred page machine learning book | 681.32.062 CAM Machine learning techniques for space weather / | 681.32.062 CHA Information, randomness & incompleteness : |
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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