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Many real-world complex systems rely on cluster synchronization to function properly. A cluster of nodes exhibits synchronous behavior while others behave erratically. Predicting the emergence of these clusters and understanding the…

Adaptation and Self-Organizing Systems · Physics 2023-09-19 Rodrigo M. Corder , Zheng Bian , Tiago Pereira , Antonio Montalban

The dynamics of a bouncing ball model under the influence of dissipation is investigated by using a two dimensional nonlinear mapping. When high dissipation is considered, the dynamics evolves to different attractors. The evolution of the…

Chaotic Dynamics · Physics 2015-10-28 André L. P. Livorati , Iberê L. Caldas , Carl P. Dettmann , Edson D. Leonel

Here we numerically study a model of excitable media, namely, a network with occasionally quiet nodes and connection weights that vary with activity on a short-time scale. Even in the absence of stimuli, this exhibits unstable dynamics,…

Disordered Systems and Neural Networks · Physics 2015-05-19 S. de Franciscis , J. J. Torres , J. Marro

The stability of synchronised networked systems is a multi-faceted challenge for many natural and technological fields, from cardiac and neuronal tissue pacemakers to power grids. In the latter case, the ongoing transition to distributed…

Adaptation and Self-Organizing Systems · Physics 2018-11-29 Frank Hellmann , Paul Schultz , Patrycja Jaros , Roman Levchenko , Tomasz Kapitaniak , Jürgen Kurths , Yuri Maistrenko

We derive simple conditions for the stability or instability of the synchronized oscillation of a class of networks of coupled phase-oscillators, which includes many of the systems used in neural modelling.

Pattern Formation and Solitons · Physics 2007-05-23 Guy Katriel

Hidden attractors are present in many nonlinear dynamical systems and are not associated with equilibria, making them difficult to locate. Recent studies have demonstrated methods of locating hidden attractors, but the route to these…

Many networks must maintain synchrony despite the fact that they operate in noisy environments. Important examples are stochastic inertial oscillators, which are known to exhibit fluctuations with broad tails in many applications, including…

Adaptation and Self-Organizing Systems · Physics 2019-12-03 Jason Hindes , Philippe Jacquod , Ira B. Schwartz

We study an extended system that without noise shows a monostable dynamics, but when submitted to an adequate multiplicative noise, an effective bistable dynamics arise. The stochastic resonance between the attractors of the…

Statistical Mechanics · Physics 2009-11-10 B. von Haeften , G. Izús , S. Mangioni , A. D. Sánchez , H. S. Wio

In this paper, we study geometric features of orientation-preserving random dynamical systems on the circle driven by memoryless noise that exhibit stable synchronisation: we consider crack points, invariant measures, and the link between…

Dynamical Systems · Mathematics 2017-08-15 Julian Newman

We study the synchronization behavior of discrete-time Markov chains on countable state spaces. Representing a Markov chain in terms of a random dynamical system, which describes the collective dynamics of trajectories driven by the same…

Dynamical Systems · Mathematics 2025-08-14 Robin Chemnitz , Maximilian Engel , Guillermo Olicón-Mendez

We study ensembles of globally coupled, nonidentical phase oscillators subject to correlated noise, and we identify several important factors that cause noise and coupling to synchronize or desychronize a system. By introducing noise in…

Adaptation and Self-Organizing Systems · Physics 2013-09-26 Yi Ming Lai , Mason A. Porter

Turbulent flows present rich dynamics originating from non-trivial energy fluxes across scales, non-stationary forcings and geometrical constraints. This complexity manifests in non-hyperbolic chaos, randomness, state-dependent persistence…

We consider a dissipative version of the standard nontwist map. Nontwist systems present a robust transport barrier, called the shearless curve, that becomes the shearless attractor when dissipation is introduced. This attractor can be…

Previous work in Boolean dynamical networks has suggested that the number of components that must be controlled to select an existing attractor is typically set by the number of attractors admitted by the dynamics, with no dependence on the…

Molecular Networks · Quantitative Biology 2024-10-11 Bryan C. Daniels , Enrico Borriello

Extreme multistability (EM) is characterized by the emergence of infinitely many coexisting attractors or continuous families of stable states in dynamical systems. EM implies complex and hardly predictable asymptotic dynamical behavior. We…

Adaptation and Self-Organizing Systems · Physics 2023-09-28 Zhen Su , Jürgen Kurths , Yaru Liu , Serhiy Yanchuk

Dynamical systems driven by Gaussian noises have been considered extensively in modeling, simulation and theory. However, complex systems in engineering and science are often subject to non-Gaussian fluctuations or uncertainties. A coupled…

Dynamical Systems · Mathematics 2009-01-19 Xianming Liu , Jinqiao Duan , Jicheng Liu , Peter E. Kloeden

The influence of noise on the generalized synchronization regime in the chaotic systems with dissipative coupling is considered. If attractors of the drive and response systems have an infinitely large basin of attraction, generalized…

For a system of globally pulse-coupled phase-oscillators, we derive conditions for stability of the completely synchronous state and all possible two-cluster states and explain how the different states are naturally connected via…

Adaptation and Self-Organizing Systems · Physics 2015-05-27 Leonhard Lücken , Serhiy Yanchuk

The asymmetrically forced, damped Duffing oscillator is introduced as a prototype model for analyzing the homoclinic tangle of symmetric dissipative systems with \textit{symmetry breaking} disturbances. Even a slight fixed asymmetry in the…

Dynamical Systems · Mathematics 2016-09-06 Anna Litvak Hinenzon , Vered Rom-Kedar

Throughout the literature on Neural Cellular Automata (NCAs), it is often taken for granted that the systems learn attractors. This is shown through evolving the system for many timesteps and noting visual similarity to the goal state.…

Neural and Evolutionary Computing · Computer Science 2026-04-15 Mia-Katrin Kvalsund , James Stovold
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