02135cam a22002418i 450099100441252880753620260108174842.0260108s2023 enka 000 0 eng 9781108842143Bibl. Dip.le Aggr. Ingegneria Innovazione - Sez. IngegneriaInnovazioneeng532.00285Data-driven fluid mechanics :combining first principles and machine learning : based on a von Karman Institute Lectires Series /Miguel A. Mendez, Andrea Ianiro, Bernd R. Noack, Steven L. BruntonCambridge :Cambridge University Press,c2023XVIII, 448 p.ill. (alcune color.) ; 24 cm"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"--Fornito dall'editoreFluid mechanicsData processingMendez, Miguel AlfonsoIaniro, Andreaauthorhttp://id.loc.gov/vocabulary/relators/aut1888560Noack, Bernd R.Brunton, Steven Lee),<1984->991004412528807536Data-driven fluid mechanics4527682UNISALENTO