000			01928cam a22003138i 4500
999			_c4388 _d4388
001			20552686
005			20200213101512.0
008			180622s2019 enk b 001 0 eng
010			_a 2018029888
020			_a9781108422093 (hardback : alk. paper)
040			_aDLC _beng _erda _cDLC
042			_apcc
050	0	0	_aTA330 _b.B78 2019
082	0	0	_a620.00285/631 _223
100	1		_aBrunton, Steven L. _q(Steven Lee), _d1984- _eauthor.
245	1	0	_aData-driven science and engineering : _bmachine learning, dynamical systems, and control / _cSteven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.
263			_a1809
300			_apages cm
504			_aIncludes bibliographical references and index.
520			_a"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"--
650		0	_aEngineering _xData processing.
650		0	_aScience _xData processing.
650		0	_aMathematical analysis.
700	1		_aKutz, Jose Nathan, _eauthor.
856			_3Full-text here _uhttps://www.cambridge.org/core/books/datadriven-science-and-engineering/77D52B171B60A496EAFE4DB662ADC36E
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK