LEADER 02135cam a22002418i 4500
001 991004412528807536
005 20260108174842.0
008 260108s2023    enka          000 0 eng  
020   $a9781108842143
040   $aBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. IngegneriaInnovazione$beng
082 00$a532.00285
245 00$aData-driven fluid mechanics :$bcombining first principles and machine learning : based on a von Karman Institute Lectires Series /$cMiguel A. Mendez, Andrea Ianiro, Bernd R. Noack, Steven L. Brunton
264  1$aCambridge :$bCambridge University Press,$cc2023
300   $aXVIII, 448 p.$bill. (alcune color.) ; 24 cm
520   $a"Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures"--$cFornito dall'editore
650  0$aFluid mechanics$xData processing
700 1 $aMendez, Miguel Alfonso
700 1 $aIaniro, Andrea$eauthor$4http://id.loc.gov/vocabulary/relators/aut$01888560
700 1 $aNoack, Bernd R.
700 1 $aBrunton, Steven Lee),$d<1984->
912   $a991004412528807536
996   $aData-driven fluid mechanics$94527682
997   $aUNISALENTO