Data-driven fluid mechanics : combining first principles and machine learning : based on a von Karman Institute lecture series / edited by Miguel A. Mendez, Andrea Ianiro, Bernd R. Noack, Steven L. Brunton.
Contributor(s): Mendez, Miguel Alfonso [editor.]
| Ianiro, Andrea [editor.] | Noack, Bernd R [editor.] | Brunton, Steven L. (Steven Lee) [editor.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2023Description: 1 online resource (xviii, 448 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108896214 (ebook).Subject(s): Fluid mechanics -- Data processingAdditional physical formats: Print version: : No titleDDC classification: 532 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 12 Jan 2023).
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.
There are no comments for this item.