Complexity measure of extreme events.
| Autor: | Das D; Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India., Ray A; Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India.; Artificial Intelligence for Climate and Sustainability, The Institute for Experiential Artificial Intelligence, Northeastern University, Portland 04101, Maine, USA., Hens C; Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad 500032, India., Ghosh D; Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India., Hassan MK; Department of Physics, Dhaka University, Dhaka 1000, Bangladesh., Dabrowski A; Division of Dynamics, Technical University of Lodz, Stefanovskiego 1/15, 90-924 Lodz, Poland., Kapitaniak T; Division of Dynamics, Technical University of Lodz, Stefanovskiego 1/15, 90-924 Lodz, Poland., Dana SK; Division of Dynamics, Technical University of Lodz, Stefanovskiego 1/15, 90-924 Lodz, Poland.; Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India. |
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| Jazyk: | angličtina |
| Zdroj: | Chaos (Woodbury, N.Y.) [Chaos] 2024 Dec 01; Vol. 34 (12). |
| DOI: | 10.1063/5.0232645 |
| Abstrakt: | Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studies while no such measure was proposed so far, how complex were the extreme events when compared to non-extreme chaos. We address here this question of complexity in extreme events and investigate if we can distinguish them from non-extreme chaotic signal. The normalized Shannon entropy in combination with disequilibrium is used for our study and it is able to distinguish between extreme chaos and non-extreme chaos and moreover, it depicts the transition points from periodic to extremes via Pomeau-Manneville intermittency and, from small amplitude to large amplitude chaos and its transition to extremes via interior crisis. We report a general trend of complexity against a system parameter that increases during a transition to extreme events, reaches a maximum, and then starts decreasing. We employ three models, a nonautonomous Liénard system, two-dimensional Ikeda map and a six-dimensional coupled Hindmarsh-Rose system to validate our proposition. (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).) |
| Databáze: | MEDLINE |
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