Product details:
| ISBN13: | 9780323950435 |
| ISBN10: | 0323950434 |
| Binding: | Paperback |
| No. of pages: | 414 pages |
| Size: | 228x152 mm |
| Language: | English |
| 700 |
Category:
Data Driven Analysis and Modeling of Turbulent Flows
Publisher: Academic Press
Date of Publication: 18 March 2025
Normal price:
Publisher's listprice:
EUR 195.00
EUR 195.00
Your price:
74 447 (70 902 HUF + 5% VAT )
discount is: 10% (approx 8 272 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
Click here to subscribe.
Availability:
Not yet published.
Long 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:
. 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.
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.
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
