000			a
999			_c32168 _d32168
008			230904b xxu||||| |||| 00| 0 eng d
020			_a9781108842143
082			_a532.05015194 _bMEN
100			_aMendez, Miguel Alfonso _eed.
245			_aData-driven fluid mechanics : combining first principles and machine learning
260			_bCambridge University Press, _c2023 _aCambridge :
300			_axviii, 448 p. ; _bill., (some col.), _c25 cm
365			_b59.99 _cGBP _d110.40
504			_aIncludes bibliographical references.
520			_aData-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.
650			_aFluid mechanics
650			_aData processing
650			_aTimeFrequency Analysis
650			_aProper Orthogonal Decomposition
650			_aKoopman Theory
650			_aMultiscale Modal Analysis
650			_aData Driven Modal Analysis
650			_aLinear Dynamical Systems
650			_aReducedOrder Modeling
650			_aTurbulence Control
700			_aIaniro, Andrea _eed.
700			_aNoack, Bernd R. _eed.
700			_aBrunton, Steven L. _eed.
942			_2ddc _cBK