Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1146/annurev-fluid-010719-060214 | ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 |
Keywords | DocType | Volume |
machine learning,data-driven modeling,optimization,control | Journal | 52 |
Issue | ISSN | Citations |
1 | 0066-4189 | 3 |
PageRank | References | Authors |
0.42 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. L. Brunton | 1 | 141 | 23.92 |
| Bernd R. Noack | 2 | 30 | 3.44 |
| Petros Koumoutsakos | 3 | 1065 | 84.99 |