Data Driven Analysis and Modeling of Turbulent Flows
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.• 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
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Data Driven Analysis and Modeling of Turbulent Flows
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.• 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
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Data Driven Analysis and Modeling of Turbulent Flows

Data Driven Analysis and Modeling of Turbulent Flows

by Karthik Duraisamy (Editor)
Data Driven Analysis and Modeling of Turbulent Flows
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Data Driven Analysis and Modeling of Turbulent Flows

Data Driven Analysis and Modeling of Turbulent Flows

by Karthik Duraisamy (Editor)

Overview

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.• 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

Product Details

ISBN-13: 9780323950442
Publisher: Elsevier Science & Technology Books
Publication date: 03/17/2025
Series: Computation and Analysis of Turbulent Flows
Sold by: Barnes & Noble
Format: eBook
Pages: 460
File size: 43 MB
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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.

Table of Contents

1. Introduction to data-driven modeling2. Modal Decomposition3. Resolvent analysis for turbulent flows4. Data assimilation and flow estimation5. Data-driven control6. Constitutive Modeling7. Parameter estimation and uncertainty quantification8. Machine Learning Augmented modeling9. Symbolic regression methods

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Explains methods for the analysis of large fields of data, and uncovering models and model improvements from numerical or experimental data on turbulence

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