000			01770nam a22002657a 4500
001			sulb0076438
003			BD-SySUS
005			20211208165322.0
008			211208s2019 enk b 001 0 eng d
020			_a9781108422093 (hardback : alk. paper)
040			_aDLC _beng _erda _cDLC _dBD-SySUS
082	0	0	_223 _a620.00285631 _bBRD
100	1		_aBrunton, Steven L. _q(Steven Lee), _d1984- _eauthor. _910987
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
264		1	_aCambridge : _bCambridge University Press, _c©2019.
300			_axxv, 472 p. : _bill. ; _c26 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"-- _cProvided by publisher.
650		0	_aEngineering _xData processing. _938268
650		0	_aScience _xData processing. _938269
650		0	_aMathematical analysis. _938270
700	1		_aKutz, Jose Nathan, _eauthor. _938271
942			_2ddc _cBK
999			_c76205 _d76205