Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence

Perspectives are presented on the use of machine learning to augment models of turbulent flows. Particular emphasis is placed on techniques that promote consistency of the machine learning model with the underlying physical model in view of the possibility of using sparse computational and experimental data. This is followed by a discussion of physics-informed and mathematical considerations on the choice of the feature space and imposition of constraints. Machine learning should be viewed as one tool in the turbulence modeler's toolkit. The associated modeling endeavor requires multidisciplinary advances.