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.
1. A roadmap for data-driven analysis and modeling of turbulent flows
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
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.
