  Recent developments in neural networks have shown the potential of estimating
drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a
large high-fidelity dataset to train neural networks is deterring their use in
practical applications. In this study, we propose a transfer learning framework
to model the drag on irregular rough surfaces even with a limited amount of
direct numerical simulations. We show that transfer learning of empirical
correlations, reported in the literature, can significantly improve the
performance of neural networks for drag prediction. This is because empirical
correlations include `approximate knowledge' of the drag dependency in
high-fidelity physics. The `approximate knowledge' allows neural networks to
learn the surface statistics known to affect drag more efficiently. The
developed framework can be applied to applications where acquiring a large
dataset is difficult, but empirical correlations have been reported.
