000			01823 a2200229 4500
003			OSt
020			_a9781108842143
040			_cIIT Kanpur
041			_aeng
082			_a532.00285 _bD262
245			_aData-driven fluid mechanics _bcombining first principles and machine learning _cedited by Miguel A. Mendez ...[et al.]
260			_bCambridge University Press _c2023 _aCambridge
300			_axviii, 448p
440			_avon Karman Institute lecture series
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"-- Provided by publisher.
650			_aFluid mechanics -- Data processing
700			_aMendez, Miguel Alfonso [ed.]
700			_aIaniro, Andrea [ed.]
700			_aNoack, Bernd R. [ed.]
700			_aBrunton, Steven L. [ed.]
942			_cBK
999			_c566722 _d566722