Data Driven Analysis and Modeling of Turbulent Flows

Karthik (Director Duraisamy

ISBN 10: 0323950434 / ISBN 13: 9780323950435

Published by Elsevier Science & Technology Jun 2025, 2025

About this Item

Neuware - 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 learningThis 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.

Seller Inventory # 9780323950435

Bibliographic Details

• Title: Data Driven Analysis and Modeling of Turbulent Flows
• Publisher: Elsevier Science & Technology Jun 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 0323950434
• ISBN 13: 9780323950435
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 228W x 155H x 23D millimeters 228 millimeters width by 155 millimeters height by 23 millimeters depth
• Weight: 660 grams

About this title

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 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.