A termék adatai:
| ISBN13: | 9780323950435 |
| ISBN10: | 0323950434 |
| Kötéstípus: | Puhakötés |
| Terjedelem: | 414 oldal |
| Méret: | 228x152 mm |
| Nyelv: | angol |
| 700 |
Témakör:
Data Driven Analysis and Modeling of Turbulent Flows
Sorozatcím:
Computation and Analysis of Turbulent Flows;
Kiadó: Academic Press
Megjelenés dátuma: 2025. március 18.
Normál ár:
Kiadói listaár:
EUR 195.00
EUR 195.00
Az Ön ára:
74 447 (70 902 Ft + 5% áfa )
Kedvezmény(ek): 10% (kb. 8 272 Ft)
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Hosszú leírás:
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.
Tartalomjegyzék:
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
