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Chaos is devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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Research Article
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January 10 2025
Martin Brešar, Ralph G. Andrzejak et al.
Detecting directional couplings from time series is crucial in understanding complex dynamical systems. Various approaches based on reconstructed state-spaces have been developed for this purpose, ...
Research Article
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January 03 2025
F. Fainstein, G. B. Mindlin et al.
We propose a method based on autoencoders to reconstruct attractors from recorded footage, preserving the topology of the underlying phase space. We provide theoretical support and test the method ...
Research Article
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January 03 2025
Rémi Delage, Toshihiko Nakata
Complex network approaches have been emerging as an analysis tool for dynamical systems. Different reconstruction methods from time series have been shown to reveal complicated behaviors that can be ...
Editor's Picks
Research Article
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January 09 2025
Ping Liu, Yong Chen et al.
We demonstrate that fundamental nonlinear localized modes can exist in the Chen–Lee–Liu equation modified by several parity-time ( P T ) symmetric complex potentials. The explicit formula ...
Research Article
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January 03 2025
Amit Sharma, Biswambhar Rakshit et al.
We investigate the aging transition in networks of excitable and self-oscillatory units as the fraction of inherently excitable units increases. Two network topologies are considered: a scale-free ...
Research Article
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December 27 2024
I. P. Longo, E. Queirolo et al.
This work deals with a parametric linear interpolation between an autonomous FitzHugh–Nagumo model and a nonautonomous skewed problem with the same fundamental structure. This paradigmatic example ...
Most Recent
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January 13 2025
Ming Zhong, Weifang Weng et al.
In this paper, we undertake a systematic exploration of soliton turbulent phenomena and the emergence of extreme rogue waves within the framework of the one-dimensional fractional nonlinear ...
Research Article
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January 10 2025
Martin Brešar, Ralph G. Andrzejak et al.
Detecting directional couplings from time series is crucial in understanding complex dynamical systems. Various approaches based on reconstructed state-spaces have been developed for this purpose, ...
Research Article
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January 10 2025
Jingyu Su, Haoyu Li et al.
Stock trend prediction is a significant challenge due to the inherent uncertainty and complexity of stock market time series. In this study, we introduce an innovative dual-branch network model ...
Ordinal Poincaré sections: Reconstructing the first return map from an ordinal segmentation of time series
Zahra Shahriari, Shannon D. Algar, et al.
Generalized synchronization in the presence of dynamical noise and its detection via recurrent neural networks
José M. Amigó, Roberto Dale, et al.
Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue
Igor Franović, Sebastian Eydam, et al.