Clustering-based Identification of Precursors of Extreme Events in Chaotic Systems
This work addresses the challenge of forecasting catastrophic events like climate patterns or rogue waves, offering a data-driven method for chaotic systems, though it appears incremental as it builds on existing clustering techniques.
The paper tackled the problem of predicting extreme events in chaotic systems by developing a clustering-based framework to identify precursor states, demonstrating its effectiveness on two chaotic models with probabilistic prediction of event occurrence.
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.