Interpreted machine learning in fluid dynamics: Explaining relaminarisation events in wall-bounded shear flows

Powerful machine learning (ML) methods are notoriously difficult to interpret. Here, we use ML methods to predict relaminarisation events in wall-bounded shear flows and obtain human-interpretable information through an explainable artificial intelligence method, the game-theoretic Shapley additive explanations (SHAP) algorithm (Lundberg & Lee, Advances in Neural Information Processing Systems, 4765 (2017)). For a proof of concept, we consider a low-dimensional model based on the self-sustaining process (SSP), where each data feature has a clear physical and dynamical interpretation in terms of representative features of the near-wall dynamics. SHAP determines that only the laminar profile, the streamwise vortex and a specific streak instability play a major role in the prediction of relaminarisation events. The method is applicable to larger datasets, in minimal plane Couette flow the prediction is based on proxies for linear streak instabilities. The SHAP analysis thus suggests that the break-up of the self-sustaining cycle is connected with a suppression of streak instabilities.