Generalized stochastic resilience for early warning signals based on Koopman operator

Miyauchi, Yuta; Ikeda, Masahiro; Kawahara, Yoshinobu

Nonlinear Dynamics, 2026, 114(4), 246

Number of Access:321（2026-10-11 14:45 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/104230

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Title
Generalized stochastic resilience for early warning signals based on Koopman operator
Creator
CreatorName
Miyauchi, Yuta
NRID
1000000749690 1000000749690
researchmap permalink
m_Ikeda m_Ikeda
Scopus Author ID
38561495900 38561495900
CreatorName
Ikeda, Masahiro
NRID
1000000514796 1000000514796
ORCID
0000-0001-7789-4709 0000-0001-7789-4709
researchmap permalink
yoshinobu.kawahara yoshinobu.kawahara
Scopus Author ID
9744960500 9744960500
CreatorName
Kawahara, Yoshinobu
Subject
Data-driven analysis
Early warning signals
Koopman operator
Local bifurcation
Residual Dynamic Mode Decomposition
Tipping phenomena
Description
Miyauchi Y., Ikeda M., Kawahara Y.. Generalized stochastic resilience for early warning signals based on Koopman operator. Nonlinear Dynamics 114, 246 (2026); https://doi.org/10.1007/s11071-025-12072-5.
Abstract
Developing methods for detecting tipping phenomena at an early stage is an important problem in various fields such as ecology, medicine, and economics. A tipping phenomenon is characterized by a rapid transition resulting from the accumulation of small parameter changes, and is known to be related to the bifurcation theory of dynamical systems. However, few studies have examined how nonlinear properties near bifurcation points affect early warning signals (EWSs) performance. In this study, we apply the Koopman operator, which describes the time evolution of dynamical systems in an infinite-dimensional function space, to generalize stochastic resilience the theoretical basis of EWSs such as variance-based ones. As a result, we develop a novel signal capable of more accurately predicting tipping events by separately isolating stochastic fluctuations induced by noise and contributions from a continuous spectrum emerging immediately above tipping points. Our experiments indicate that our proposed method provides robust early warning detection across diverse datasets and is notably resilient to observation noise, often performing competitively with conventional indicators.
Publisher
Springer Science and Business Media B.V.
Source Title
Nonlinear Dynamics
Volume (Issue)
114(4)
Page
246
Date of Issued
2026-02-24
Language
English
Handle URL
https://hdl.handle.net/11094/104230
PISSN
0924090X
eISSN
1573269X
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1007/s11071-025-12072-5
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article