Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- A Note on the Notation
- Part I Motivation
- Part II Methods from Signal Processing
- Part III Data-Driven Decompositions
- Part IV Dynamical Systems
- Part V Applications
- 14 Machine Learning for Reduced-Order Modeling
- 15 Advancing Reacting Flow Simulations with Data-Driven Models
- 16 Reduced-Order Modeling for Aerodynamic Applications and Multidisciplinary Design Optimization
- 17 Machine Learning for Turbulence Control
- 18 Deep Reinforcement Learning Applied to Active Flow Control
- Part VI Perspectives
- References
16 - Reduced-Order Modeling for Aerodynamic Applications and Multidisciplinary Design Optimization
from Part V - Applications
Published online by Cambridge University Press: 12 January 2023
Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- A Note on the Notation
- Part I Motivation
- Part II Methods from Signal Processing
- Part III Data-Driven Decompositions
- Part IV Dynamical Systems
- Part V Applications
- 14 Machine Learning for Reduced-Order Modeling
- 15 Advancing Reacting Flow Simulations with Data-Driven Models
- 16 Reduced-Order Modeling for Aerodynamic Applications and Multidisciplinary Design Optimization
- 17 Machine Learning for Turbulence Control
- 18 Deep Reinforcement Learning Applied to Active Flow Control
- Part VI Perspectives
- References
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- Data-Driven Fluid MechanicsCombining First Principles and Machine Learning, pp. 330 - 349Publisher: Cambridge University PressPrint publication year: 2023