01290nam a22001817a 4500008004500000020001800045082001800063100002000081245009100101250001200192260004100204300001600245520072600261650003600987650002701023650002601050700003201076230427b     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