Predictability and suppression of extreme events in complex systems

TL;DR

This paper investigates the mechanisms behind extreme events in complex systems of coupled chaotic oscillators, demonstrating their predictability and controllability through real-time forecasting and small perturbations.

Contribution

It identifies the specific mechanism causing extreme events and introduces methods for their real-time prediction and suppression in complex systems.

Findings

Extreme events deviate from power-law distributions due to specific mechanisms.

Real-time forecasting of extreme events is feasible based on identified mechanisms.

Small perturbations can effectively suppress impending extreme events.

Abstract

In many complex systems, large events are believed to follow power-law, scale-free probability distributions, so that the extreme, catastrophic events are unpredictable. Here, we study coupled chaotic oscillators that display extreme events. The mechanism responsible for the rare, largest events makes them distinct and their distribution deviates from a power-law. Based on this mechanism identification, we show that it is possible to forecast in real time an impending extreme event. Once forecasted, we also show that extreme events can be suppressed by applying tiny perturbations to the system.