Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning New Edition

Data-Driven Fluid Mechanics Combining First Principles and Machine Learning Miguel A. Mendez (ed.), Andrea Ianiro (ed.), Bernd R. Noack (ed.), Steven L. Brunton (ed.)

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.

About

Miguel A. Mendez: Miguel A. Mendez is a researcher or professional known in various fields, potentially including but not limited to engineering, mathematics, or applied sciences. He might have contributions in research, publications, or projects related to his field of expertise, possibly in collaboration with other experts.

Andrea Ianiro: Andrea Ianiro is likely a professional or researcher recognized in specific domains, which could encompass engineering, mathematics, or related scientific fields. His contributions might involve research, publications, or projects, potentially focusing on areas that intersect with fluid dynamics, control theory, or other related disciplines.

Bernd R. Noack: Bernd R. Noack is a prominent figure known particularly in the field of fluid mechanics. He might have made significant contributions to this field through research, publications, or developments in methodologies related to fluid dynamics, possibly with a focus on turbulence, flow control, or related areas.

Steven L. Brunton: Steven L. Brunton is recognized for his expertise in dynamical systems and control. He likely has substantial contributions in research, publications, or advancements within these fields, which may include nonlinear dynamics, control theory, data-driven modeling, or system identification. His work might encompass applications in various domains, such as engineering or physics.

Product details

• Publisher: Cambridge University Press
• Published: February 2023
• ISBN: 9781108902267
• Title: Data-Driven Fluid Mechanics
• Author: Miguel A. Mendez (ed.); Andrea Ianiro (ed.); Bernd R. Noack (ed.); Steven L. Brunton (ed.)
• Imprint: Cambridge University Press
• Language: English
• Number of Pages: 470