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Recent progress in augmenting turbulence models with physics-informed machine learning
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DOI：10.1007/s42241-019-0089-y.png)
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In view of the long stagnation in traditional turbulence modeling, researchers have attempted using machine learning to augment turbulence models. This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning. We also discuss our works on ensemble-based field inversion to provide training data for constructing machine learning models. Future and on-going research efforts are introduced.
Keyword:
Machine learning
turbulence modeling
data-driven modeling
model uncertainty
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