000 02023cam a22003618i 4500
001 20552686
003 OSt
005 20210518113515.0
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 _a8870
_223
_bTA 330 .B78 2019
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
264 1 _aCambridge :
_bCambridge University Press,
_c2019.
300 _apages cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
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.
650 0 _aScience
_xData processing.
650 0 _aMathematical analysis.
700 1 _aKutz, Jose Nathan,
_eauthor.
906 _a7
_bcbc
_corignew
_d1
_eecip
_f20
_gy-gencatlg
942 _2ddc
_cBK
955 _brk21 2018-06-22
_crk21 2018-06-22 to rk09 for review
999 _c10296
_d10296