Further Details

Title: Data-Driven Fluid Mechanics
Condition: New
Subtitle: Combining First Principles and Machine Learning
Author: Andrea Ianiro
Contributor: Andrea Ianiro (Edited by), Bernd R. Noack (Edited by), Steven L. Brunton (Edited by), Miguel A. Mendez (Edited by)
Type: Hardback
Format: Hardback
EAN: 9781108842143
ISBN: 9781108842143
Publisher: Cambridge University Press
Genre: Science Nature & Math
Release Date: 02/02/2023
Country/Region of Manufacture: GB
Item Height: 251mm
Item Length: 176mm
Item Width: 25mm
Item Weight: 1020g
Language: English
Release Year: 2023
Description: 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.

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