Data Driven Analysis and Modeling of Turbulent Flows
Academic Press Inc (Verlag)
9780323950435 (ISBN)
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.
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.
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
| Erscheinungsdatum | 23.04.2025 |
|---|---|
| Reihe/Serie | Computation and Analysis of Turbulent Flows |
| Verlagsort | Oxford |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 670 g |
| Themenwelt | Naturwissenschaften ► Physik / Astronomie ► Thermodynamik |
| ISBN-13 | 9780323950435 / 9780323950435 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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