Data Driven Analysis and Modeling of Turbulent Flows

Erscheinungsjahr 2025
ISBN: 978-0-323-95043-5
Verlag: Anderson Publishing

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 670 g

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.

Autoren/Hrsg.

Duraisamy, Karthik

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Naturwissenschaften Physik Mechanik Kontinuumsmechanik, Strömungslehre
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Strömungslehre
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

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

Über Autor(innen)

Duraisamy, Karthik
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.