Data Driven Analysis and Modeling of Turbulent Flows
- 1st Edition - March 17, 2025
- Latest edition
- Editor: Karthik Duraisamy
- Language: English
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… Read more
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Description
Description
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:
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
Key features
Key features
- 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
Readership
Readership
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.
Table of contents
Table of contents
1. Introduction to data-driven modeling
2. Modal Decomposition
3. Resolvent analysis for turbulent flows
4. Data assimilation and flow estimation
5. Data-driven control
6. Constitutive Modeling
7. Parameter estimation and uncertainty quantification
8. Machine Learning Augmented modeling
9. Symbolic regression methods
2. Modal Decomposition
3. Resolvent analysis for turbulent flows
4. Data assimilation and flow estimation
5. Data-driven control
6. Constitutive Modeling
7. Parameter estimation and uncertainty quantification
8. Machine Learning Augmented modeling
9. Symbolic regression methods
Product details
Product details
- Edition: 1
- Latest edition
- Published: March 17, 2025
- Language: English
About the editor
About the editor
KD
Karthik Duraisamy
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.
Affiliations and expertise
Director, Center for Data-driven Computational Physics and the Air Force Center for Rocket Combustor Dynamics, University of Michigan, USAView book on ScienceDirect
View book on ScienceDirect
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