A physics-aware machine to predict extreme events in turbulence
This work addresses the challenge of forecasting abrupt transitions in chaotic systems like turbulence, which is incremental as it integrates existing methods for improved performance.
The paper tackles the problem of predicting extreme events in turbulent flows by combining reservoir computing with physical conservation laws, achieving accurate predictions of occurrence and amplitude that neither approach could achieve alone.
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.