000			01338nam a22002057a 4500
999			_c12348 _d12348
008			230427b 2022 ||||| |||| 00| 0 eng d
020			_a9781009098489
082			_a620.0028 _bBRU
100			_aBrunton, Steven
245			_aData-driven science and engineering : machine learning, dynamical systems, and control
250			_a2nd ed.
260			_aUK _bCAMBRIDGE UNIVERSITY PRESS _c2022
300			_axxiv, 590p.
520			_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.
650			_aSparsity and compressed sensing
650			_aReinforcement Learning
650			_aLinear Control Theory
700			_aKutz, J. Nathan _eCo-author
942			_cBK