01500nam a22002177a 4500999001700000008004500017020001800062082001800080100002000098245009100118250001200209260004100221300001600262520072600278650003601004650002701040650002601067700003201093942000701125952015001132  c12348d12348230427b     2022      ||||| |||| 00| 0 eng d  a9781009098489  a620.0028bBRU  aBrunton, Steven  aData-driven science and engineering : machine learning, dynamical systems, and control  a2nd ed.  aUKbCAMBRIDGE UNIVERSITY PRESSc2022  axxiv, 590p.  aData-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.  aSparsity and compressed sensing  aReinforcement Learning  aLinear Control Theory  aKutz, J. Nathan eCo-author  cBK  00102ddc406620_002800000000000_BRU708NFIC915458aMUbMUcGENd2023-04-27l1o620.0028 BRUpMPC6651r2025-03-17s2024-08-02yBKzColumn - 24