Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

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Steven L. Brunton, J. Nathan KutzCambridge University Press, 2/28/2019EAN 9781108422093, ISBN10: 1108422098Hardcover, 492 pages, 26.1 x 18.4 x 2.4 cmLanguage: EnglishData-driven di scovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling 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.Part I. Dimensionality Reduction and Transforms1. Singular value decomposition2. Fourier and wavelet transforms3. Sparsity and compressed sensingPart II. Machine Learning and Data Analysis4. Regression and model selection5. Clustering and classification6. Neural networks and deep learningPart III. Dynamics and Control7. Data-driven dynamical systems8. Linear control theory9. Balanced models for control10. Data-driven controlPart IV. Reduced-Order Models11. Reduced-order models (ROMs)12. Interpolation for parametric ROMs. Show more