04095nam 2200565 i 45000010014000000030005000140050017000190060019000360070015000550080041000700100031001110200030001420200025001720280016001970350025002130350021002380400040002590500025002990820015003241000032003392450150003712640150005213000028006713360021006993370026007203380032007464900040007785040051008185050381008695060072012505201460013225300037027825380036028195380047028555880054029026500032029566500027029886530031030156530019030466530025030656530027030906530020031177000096031377000033032337000040032667100064033067760053033708300041034238560065034649781611974508SIAM20170228190106.0m eo d cr bn |||m|||a170224s2016 pau ob 001 0 eng d a 2016026184z 2016024120 a9781611974508qelectronic z9781611974492qprint50aOT149bSIAM a(CaBNVSL)thg00972502 a(OCoLC)951190550 aCaBNVSLbengerdacCaBNVSLdCaBNVSL 4aQA402.2b.K88 2016eb04a518/.22231 aKutz, Jose Nathan,eauthor.10aDynamic mode decomposition :bdata-driven modeling of complex systems /cJ. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor. 1aPhiladelphia, Pennsylvania :bSociety for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104),c[2016] a1 PDF (xvi, 234 pages). atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aOther titles in applied mathematics aIncludes bibliographical references and index.0 aPreface -- 1. Dynamic mode decomposition : an introduction -- 2. Fluid dynamics -- 3. Koopman analysis -- 4. Video processing -- 5. Multiresolution DMD -- 6. DMD with control -- 7. Delay coordinates, ERA, and hidden Markov models -- 8. Noise and power -- 9. Sparsity and DMD -- 10. DMD on nonlinear observables -- 11. Epidemiology -- 12. Neuroscience -- 13. Financial trading. aRestricted to subscribers or individual electronic text purchasers.3 aData-driven dynamical systems is a burgeoning field--it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations. aAlso available in print version. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat Reader. aDescription based on title page of print version. 0aDecomposition (Mathematics) 0aMathematical analysis. aDynamic Mode Decomposition aKoopman theory aData-driven modeling aEquation-free modeling aComplex systems1 aBrunton, Steven L.c(Professor of mechanical enginerring and applied mathematics),eauthor.1 aBrunton, Bingni W.,eauthor.1 aProctor, Joshua L.,d1982-eauthor.2 aSociety for Industrial and Applied Mathematics,epublisher.08iPrint version:w(DLC) 2016024120z9781611974492 0aOther titles in applied mathematics.403SIAMuhttp://epubs.siam.org/doi/book/10.1137/1.9781611974508