Predicting drag on rough surfaces by transfer learning of empirical correlations

In this presentation, we discuss how to model the drag on irregular rough surfaces using neural networks when only a limited amount of high-fidelity data is available. We propose a transfer learning framework that pre-trains neural networks with empirical correlations and fine-tunes them with a few direct numerical simulation data. We found that pre-training neural networks with empirical correlations can significantly improve the generalization ability of neural networks. The developed framework can be applied to applications where acquiring a large dataset is difficult, but empirical correlations have been reported.