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  Extreme events in a class of nonlinear Duffing-type oscillators with a parametric periodic force

Zhao, D., Li, Y., Xu, Y., Liu, Q., Kurths, J. (2022): Extreme events in a class of nonlinear Duffing-type oscillators with a parametric periodic force. - European Physical Journal Plus, 137, 314.
https://doi.org/10.1140/epjp/s13360-022-02530-z

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 Creators:
Zhao, Dan1, Author
Li, Yongge1, Author
Xu, Yong1, Author
Liu, Qi1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Extreme events happen when a system is far from the expectation and normal region, which are common in many practical problems, such as climate and engineering systems. It will affect the accuracy and damage the reliability, or even lead to a collapse of the system. In this work, extreme events are studied in a class of generalized nonlinear Duffing-type oscillators with a parametric periodic force. The occurrence mechanism, description methods and risk of extreme events are discussed. We find that the tail probability of the state response is large when extreme events occur frequently. This indicates that the dynamic structure enables the system to reach a rather far position, for which the varying of the potential function provides a possible underlying explanation for this phenomenon. In addition, the effects of the amplitude and the frequency are investigated to quantify the extreme events. With the metrics of inter-event interval (IEI), mean of IEI, survival probability function, and hazard rate function, the risk of extreme events is characterized. The obtained results not only quantitatively give the characteristics of extreme events in a class of generalized Duffing-type oscillators, but also assess the risk of extreme events, which can provide theoretical guidance for the design and fabrication of micro-electromechanical components.

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Language(s): eng - English
 Dates: 2022-03-072022-03
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1140/epjp/s13360-022-02530-z
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Weather
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: European Physical Journal Plus
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 137 Sequence Number: 314 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals139
Publisher: Springer