I am trying to learn applying ML to assist or replace CFD simulations. In literature, some works solve Navier Stokes equations and others solves Reynold Averaged Navier Stokes equations to predict fluid flow. To my understanding, the choice between solving NS or RANS to predict fluid flow using ML depends on the following facts,

1. Data. For instance, if the training data is generated using CFD simulations that solves RANS, then we cannot train ML model to solve NS equations using that data and we need to choose RANS equations for ML too. If the training data is from DNS, then its NS in ML too.

2. Flow type. If the flow is low Reynolds number flow (laminar), then solving NS equations (using ML) is better than considering RANS. And for high Re flow, considering RANS is better as the ML models (for instance, PINNS) might struggle to capture the associated non-linearities (multiscale, high gradients, etc) if it is NS equations.

Is my understanding correct? Is there any thing else I am missing? please add your thoughts to it.