Emergent behavior in dynamically coupled oscillators and its application in detecting exogenous signals

In this paper, we investigate the onset of emergent behavior in a ring network of unidirectionally coupled Duffing oscillators, where the interaction takes place via a second order dynamical system. It is demonstrated that, with the proposed coupling scheme, the network exhibits richer dynamic behavior than in the traditional case, where the interaction is via diffusive and static couplings. In particular, it is shown that there exists a highly sensitive region in the parameter space of the dynamic coupling, where the network has different behaviors, ranging from chaotic and quasiperiodic motion to periodic limit cycles. In the study, these behaviors are characterized by using bifurcation diagrams, Lyapunov exponents, and Poincaré sections. Additionally, a potential application of the obtained results for sensing is discussed. Specifically, it is shown that, by operating within the highly sensitive region mentioned above, the network alters considerably its dynamic state when an arbitrary exogenous signal is applied to it, i.e., the network can be used as a sensor for detecting whether a given signal of interests is present or absent. • We investigate the onset of emergent behavior in a network of Duffing oscillators. • In our approach, the oscillators are dynamically coupled. • The proposed network exhibits rich dynamic behavior. • The obtained results may be useful for sensing applications.

Authors

Institutions

Publication Details

Journal
Chaos Solitons & Fractals
Published
2025-11-17
DOI
https://doi.org/10.1016/j.chaos.2025.117608
Primary Topic
Chaos control and synchronization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Emergent behavior in dynamically coupled oscillators and its application in detecting exogenous signals

L.J. Ontañón-Garcı́a, G. Huerta-Cuéllar, Jonatán Peña Ramírez, J.J. Barba-Franco et al.
Chaos Solitons & Fractals
Chaos control and synchronization
article

Emergent behavior in dynamically coupled oscillators and its application in detecting exogenous signals

L.J. Ontañón-Garcı́a, G. Huerta-Cuéllar, Jonatán Peña Ramírez, J.J. Barba-Franco, R. Jaimes-Reátegui, J.A. Villalobos Torres
article en

Abstract

In this paper, we investigate the onset of emergent behavior in a ring network of unidirectionally coupled Duffing oscillators, where the interaction takes place via a second order dynamical system. It is demonstrated that, with the proposed coupling scheme, the network exhibits richer dynamic behavior than in the traditional case, where the interaction is via diffusive and static couplings. In particular, it is shown that there exists a highly sensitive region in the parameter space of the dynamic coupling, where the network has different behaviors, ranging from chaotic and quasiperiodic motion to periodic limit cycles. In the study, these behaviors are characterized by using bifurcation diagrams, Lyapunov exponents, and Poincaré sections. Additionally, a potential application of the obtained results for sensing is discussed. Specifically, it is shown that, by operating within the highly sensitive region mentioned above, the network alters considerably its dynamic state when an arbitrary exogenous signal is applied to it, i.e., the network can be used as a sensor for detecting whether a given signal of interests is present or absent. • We investigate the onset of emergent behavior in a network of Duffing oscillators. • In our approach, the oscillators are dynamically coupled. • The proposed network exhibits rich dynamic behavior. • The obtained results may be useful for sensing applications.

Chaos Solitons & FractalsVol. 202
Universidad de Guadalajara (MX), Autonomous University of San Luis Potosí (MX), Tecnológico Nacional de México (MX), Universidad Enrique Díaz de León (MX), Centro de Investigación Científica y de Educación Superior de Ensenada (MX)
Openalex Percentile: Top 72%
Chaos control and synchronization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.