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Machine Learning for Fluid
Mechanics
Brunton, Steven L ; Noack, Bernd R ; Koumoutsakos, Petros
Annual review of fluid
mechanics
, 2020-01, Vol.52 (1), p.477-508
[Periódico revisado por pares]
Annual Reviews
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Título:
Machine Learning for Fluid
Mechanics
Autor:
Brunton, Steven L
;
Noack, Bernd R
;
Koumoutsakos, Petros
Assuntos:
Computer Science
;
control
;
data-driven modeling
;
Engineering Sciences
;
Fluids
mechanics
;
Machine Learning
;
Mechanics
;
Nonlinear Sciences
;
optimization
É parte de:
Annual review of fluid
mechanics
, 2020-01, Vol.52 (1), p.477-508
Descrição:
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.
Editor:
Annual Reviews
Idioma:
Inglês
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