Searcharxiv⌕ Search

arXiv · 2610.08239

Heterogeneity-induced chaos in globally coupled Stuart--Landau oscillators

Abstract

We show that linear-growth-rate heterogeneity combined with complex global coupling can generate quasiperiodicity and chaos in a finite population of Stuart-Landau oscillators with identical natural frequencies and no Kerr-type nonlinear frequency shift. Lyapunov spectra identify limit cycles, quasiperiodic tori, and chaotic attractors, while the order-parameter amplitude exhibits signatures consistent with period-doubling-like transitions towards chaos. We further derive a finite-size invariant manifold of vanishing-order-parameter phase-locked solutions represented by closed polygons with fixed side lengths and demonstrate their configuration-dependent transverse stability. Ensemble sampling reveals pronounced coexistence among polygonal and ordinary phase-locked cycles, quasiperiodic tori, and chaotic attractors. An amplitude-time rescaling identifies the leading parameter approximately organizing the regime boundaries and the similar bifurcation and multistability patterns. These results advance our understanding of collective dynamics in amplitude-inclusive oscillator networks by revealing how heterogeneity and complex coupling jointly shape dynamical complexity, bifurcation structure, and multistability.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luekai Zhao, Nariya Uchida. 2026-10-06. Heterogeneity-induced chaos in globally coupled Stuart--Landau oscillators. https://arxiv.org/abs/2610.08239

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Slow-spectrum preshaping for reservoir computing: Attractor reconstruction in attracting submanifolds under partial observation

Data-driven reproduction of chaotic dynamics under partial observation remains a challenge despite its practical importance. Reservoir computing (RC) and other data-driven approaches often succeed in short-term prediction but are sensitive to hyperparameters and frequently fail to reproduce the long-term statistical properties of the system. As recently shown, a major cause of this failure is spurious slow modes induced by training, which render the reconstructed invariant set transversally unstable in the representation space. To address this, a design principle called Attractor Reconstruction in Attracting Submanifolds (ARAS) was proposed, which requires the reconstruction to lie in a low-dimensional, transversally attracting submanifold. Under full observation, ARAS was realized by an input-layer design that limits the number of slow modes perturbed during training and anchors the remaining modes to stay fast, thereby suppressing spurious slow modes. However, under partial observation, this design alone fails because the submanifold designed to be attracting cannot retain the observation history needed to unfold the observed data into a faithful reconstruction. In this study, we propose slow-spectrum preshaping, which introduces a slow spectrum into the reservoir prior to feeding input data. The induced slow modes then retain a memory of past observations within the submanifold, while the input-layer design makes the submanifold transversally attracting, so that ARAS is realized under partial observation. Through numerical experiments on various chaotic systems, we show that our approach enables reliable short-term prediction and long-term reproduction of chaos over a wide range of hyperparameters without a posteriori tuning.

nlin.CD↗

Number Theory of Decaying Turbulence 1: Operator Representation and Universality

We derive a formal statistical solution of freely decaying incompressible turbulence in arbitrary dimension \(d>1\) using Navier--Stokes loop equations. The loop Fourier transform maps smooth deterministic Cauchy data in infinite space to an oscillatory amplitude of a one-dimensional momentum-loop quantum field theory. In bounded-variation calculus the nonlinear advection term becomes a closed-loop total derivative and cancels on the compact spherical target, leaving a diffusive momentum-loop evolution. Its exact decaying solution is the planar Euler ensemble of rational star-polygon walks, whose continuum limit splits into parity classes, \(η=N\bmod 2\). In logarithmic time the Euler ensemble is a fixed point of the compact momentum-loop dynamics. The even ensemble carries an alternating unstable mode with Lyapunov exponent \(λ=\cot^2(πp/q)>0\), while the odd representatives have no local shape instabilities. For \(d>2\) the planar ensemble is a slice of a degenerate manifold of equal-step spherical polygons. The transverse deformations along this manifold are exact zero modes; integrating over them gives a singular Wilson-loop functional, so they are projected out of the admissible ensemble. The normal edge-length defects are strictly stable, with the universal angular Laplacian as the leading continuum operator. The odd Euler ensemble is therefore the locally stable turbulent attractor in every dimension. From the velocity correlation of the ensemble we prove that its energy spectrum does not depend on the dimension \(d>1\): two- and three-dimensional decaying turbulence share one scaling function. The spectrum and its Riemann-zeta structure are derived in the second paper of this series, and the comparison with simulations and experiments is given in the third.

nlin.CD↗

On a cross-coupling of Rulkov neural maps

We introduce a novel coupling of Rulkov neural maps, proposing a heuristic biological interpretation for the transition to non-small values of the perturbations acting on the slow variables. We analytically prove that the coupling of two identical neurons preserves the emergence of Devaney chaos through the existence of a generalized snap-back repeller, provided that a snap back repeller exists for the original system. We present numerical simulations for the coupling of two different neurons showing the arising of a potential global strange attractor, whose fractal structure is strongly suggested by the computation of a non-integer Kaplan-Yorke dimension. Furthermore, we perform standard numerical studies concerning time series, Lyapunov exponents spectra, bifurcation diagrams and basins of attraction. Finally, we briefly propose a generalization of the coupling to an arbitrary number of neurons.

nlin.CD↗