Data-driven science and engineering : machine learning, dynamical systems, and control / Steven L. Brunton and J. Nathan Kutz.
Material type: TextPublisher: Cambridge, England : Cambridge University Press, 2019Description: xxii, 472 pages : illustrations ; 26 cmContent type:- text
- unmediated
- volume
- 9781108422093
- 620.00285631 B838d 23
- TA330 .B78 2019
Item type | Current library | Shelving location | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|
Books | Main Library-Nabua | Graduate School Library | GRD 620.00285631 B838d 2019 (Browse shelf(Opens below)) | 1-1 | Available | 026269 |
Includes bibliographical references and index.
Singular value decomposition (SVD) -- Fourier and wavelet transforms -- Sparsity and compressed sensing -- Regression and model selection -- Clustering and classification -- Neural networks and deep learning -- Data - driven dynamical systems -- Linear control theory -- Balanced models for control -- Data - driven control -- Reduced order models (ROMs) -- Interpolation for parametric ROMs.
"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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