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008			231113s2022 nyua frb 001 0 eng c
020			_a9781009098489
040			_aDIBRA _beng _erda _cUVAL
041	0		_aeng
082	0	4	_a620.00285
100	1		_aBrunton, Steven L. _q(Steven Lee), _d1984- _eautor. _9241003.
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.
250			_aSecond edition.
264		1	_aCambridge : _bCambridge University Press, _c2022.
300			_axxiv, 590 páginas.
500			_aIncluye índice.
500			_aGlosario: páginas 542-551.
504			_aBibliografía: páginas 552-587.
520			_a"Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material including lecture videos per section, homeworks, data, and codes in MATLAB, Python, and Julia available on databookuw.com"-- _cProvided by publisher.
650	1	4	_aINGENIERIA _xPROCESAMIENTO DE DATOS _98960.
700	1		_aKutz, Jose Nathan, _e, autor. _9241004.
942			_2ddc _cBK
999			_c288289 _d288289