Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- A Note on the Notation
- Part I Motivation
- 1 Analysis, Modeling, and Control of the Cylinder Wake
- 2 Coherent Structures in Turbulence: A Data Science Perspective
- 3 Machine Learning in Fluids: Pairing Methods with Problems
- Part II Methods from Signal Processing
- Part III Data-Driven Decompositions
- Part IV Dynamical Systems
- Part V Applications
- Part VI Perspectives
- References
3 - Machine Learning in Fluids: Pairing Methods with Problems
from Part I - Motivation
Published online by Cambridge University Press: 12 January 2023
- Frontmatter
- Contents
- Contributors
- Preface
- A Note on the Notation
- Part I Motivation
- 1 Analysis, Modeling, and Control of the Cylinder Wake
- 2 Coherent Structures in Turbulence: A Data Science Perspective
- 3 Machine Learning in Fluids: Pairing Methods with Problems
- Part II Methods from Signal Processing
- Part III Data-Driven Decompositions
- Part IV Dynamical Systems
- Part V Applications
- Part VI Perspectives
- References
Summary
- Type
- Chapter
- Information
- Data-Driven Fluid MechanicsCombining First Principles and Machine Learning, pp. 34 - 58Publisher: Cambridge University PressPrint publication year: 2023