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

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Abrupt and rapid high-amplitude changes in a dynamical system’s states known as extreme events 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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science - ICCS2023
Subtitle of host publicationComputational Science – ICCS 2023 23rd International Conference, Prague, Czech Republic, July 3–5, 2023, Proceedings, Part IV
EditorsDmitry V. Kozyrev
ISBN (Electronic)978-3-031-50482-2
Publication statusPublished - 2023
Event23rd International Conference on Computational Science, ICCS 2023 - Prague, Czech Republic
Duration: 3 Jul 20235 Jul 2023

Publication series

NameLecture Notes in Computer Science - ICCS2023
ISSN (Electronic)1611-3349


Conference23rd International Conference on Computational Science, ICCS 2023
Country/TerritoryCzech Republic

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


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