Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

Abstract

We present a comprehensive framework for augmenting turbulence models with physics-informed machine learning, illustrating a complete workflow from identification of input/output to prediction of mean velocities. The learned model has Galilean invariance and coordinate rotational invariance.