.png){kind=icon}
Return
Enhancing computational fluid dynamics with machine learning
.png){kind=icon}
OA
AI
DOI：10.1038/s43588-022-00264-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. Here we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account.
Keywords:
TURBULENT-BOUNDARY-LAYERS
NEURAL-NETWORKS
MODEL-REDUCTION
FLOW
PROJECTION
IDENTIFICATION
EQUATIONS
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K
Organization
Cited Papers
Electrical characteristics of AlxGa1−xN Schottky diodes prepared by a two-step surface treatment
PREAI
ShareSaveAn interpretable framework of data-driven turbulence modeling using deep neural networks.png)
PHYSICS OF FLUIDS
OAAI
PHYSICS OF FLUIDSIF4.3
OAAIShareSaveShareSaveTraining convolutional neural networks to estimate turbulent sub-grid scale reaction ratesCOMBUSTION AND FLAMEOAAI
COMBUSTION AND FLAMEIF6.2
OAAIShareSaveShareSave
View more