Data Driven Analysis and Modeling of Turbulent Flows (Computation and Analysis of Turbulent Flows) - Softcover

Softcover

ISBN 10: 0323950434 ISBN 13: 9780323950435

Publisher: Academic Press, 2025

Synopsis

Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science.

The book is organized into three parts:

• Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods
• Methods for estimation and control using data assimilation and machine learning approaches
• Finally, novel modeling techniques that combine physical insights with machine learning

This book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.

• Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods
• Methods for estimation and control using data assimilation and machine learning approaches
• Finally, novel modeling techniques that combine physical insights with machine learning

About this title

About the Author

Karthik Duraisamy is a professor of Aerospace Engineering and the director of the Michigan Institute for Computational Discovery at the University of Michigan, Ann Arbor, USA. His research interests are in data-driven and reduced order modeling, statistical inference, numerical methods, and Generative AI with application to fluid flows. He is also the founder and Chief Scientist of the Silicon Valley startup Geminus.AI which is focused on physics informed AI for industrial decision-making.

• Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods
• Methods for estimation and control using data assimilation and machine learning approaches
• Finally, novel modeling techniques that combine physical insights with machine learning

This book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.

Edition details

• Publisher: Academic Press
• Publication date: 2025
• Language: English
• ISBN 10: 0323950434
• ISBN 13: 9780323950435
• Binding: Paperback
• Number of pages: 414
• Editor: Duraisamy