Clustering-based Identification of Precursors of Extreme Events in Chaotic Systems

TL;DR

This paper presents a clustering-based data-driven framework to identify and predict precursors of extreme events in chaotic systems, demonstrated on turbulence and flow models, improving understanding of pathways to such events.

Contribution

The study introduces a novel clustering and probabilistic approach to detect and predict extreme event precursors in chaotic systems, validated on two complex models.

Findings

Successfully identified precursor states leading to extreme events.

Provided a probabilistic framework for predicting extreme events.

Demonstrated effectiveness on turbulence and flow models.

Abstract

Abrupt and rapid high-amplitude changes in a dynamical system's states known as extreme event appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. However, because of their chaotic nature, their modelling represents a great challenge up to this day. The applicability of a data-driven modularity-based clustering technique to identify precursors of rare and extreme events in chaotic systems is here explored. The proposed identification framework based on clustering of system states, probability transition matrices and state space tessellation was developed and tested on two different chaotic systems that exhibit extreme events: the Moehliss-Faisst-Eckhardt model of self-sustained turbulence and the 2D Kolmogorov flow. Both exhibit extreme events in the form of bursts in kinetic energy and dissipation. It is shown that the proposed framework provides a way to identify pathways towards extreme events and predict their occurrence from a probabilistic standpoint. The clustering algorithm correctly identifies the precursor states leading to extreme events and allows for a statistical description of the system's states and its precursors to extreme events.