  Machine Learning (ML) is becoming increasingly popular in fluid dynamics.
Powerful ML algorithms such as neural networks or ensemble methods are
notoriously difficult to interpret. Here, we use the novel Shapley Additive
Explanations (SHAP) algorithm (Lundberg & Lee, 2017), a game-theoretic approach
that explains the output of a given ML model, to ascertain which physical
processes are significant in the prediction of relaminarisation events in
wall-bounded parallel shear flows. The flow is described by an established
low-dimensional model whose variables have a clear physical and dynamical
interpretation in terms of known representative features of the near-wall
dynamics, i.e. streamwise vortices, streaks and linear streak instabilities. We
consistently find only three modes to play a major role in the prediction: the
laminar profile, the streamwise vortex, and a specific streak instability. SHAP
thus distinguishes significant from representative features, hence we
demonstrate that it is an explainable AI method which can provide useful and
human-interpretable insight for fluid dynamics.
