Annual Review of Fluid Mechanics· 2019Q1· Review
Machine Learning for Fluid Mechanics
- 2,675citations
- Q1SCImago
- 2019year
Short summary
Machine learning (ML) techniques are increasingly vital for extracting knowledge from the vast data generated in fluid mechanics, offering new ways to understand, model, and control fluid flows.
AI-generated from the title and abstract; the full text is not read.
Key points
- ML extracts information from large datasets in fluid mechanics (experiments, simulations, field measurements).
- ML techniques can augment domain knowledge and automate flow control/optimization.
- The article reviews ML methodologies for understanding, modeling, and controlling fluid flows.
- ML offers potential to transform fluid mechanics research and industrial applications.
AI-generated from the title and abstract; the full text is not read.
Abstract
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.
The authors' abstract, as published at the source. Annual Review of Fluid Mechanics, 2019 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy