  We propose a physics-aware machine learning method to time-accurately predict
extreme events in a turbulent flow. The method combines two radically different
approaches: empirical modelling based on reservoir computing, which learns the
chaotic dynamics from data only, and physical modelling based on conservation
laws. We show that the combination of the two approaches is able to predict the
occurrence and amplitude of extreme events in the self-sustaining process in
turbulence-the abrupt transitions from turbulent to quasi-laminar states-which
cannot be achieved by using either approach separately. This opens up new
possibilities for enhancing synergistically data-driven methods with physical
knowledge for the accurate prediction of extreme events in chaotic dynamical
systems.
