Search arXivSearch

arXiv · 2403.17386

A framework to identify supercritical and subcritical Turing bifurcations: Case study of a system sustaining cubic and quadratic autocatalysis

Abstract

In this work, we focus on an autocatalytic reaction-diffusion model and carry out multiple scale weakly nonlinear analysis. A cubic and a quadratic autocatalytic reaction system is analysed. We develop a framework to identify the critical surfaces in parameter space across which the nature of the Turing bifurcation changes from supercritical to subcritical. These are verified by direct numerical simulations of the system. Using weakly nonlinear analysis, we derive equations up to the fifth order that governs the amplitude of the spatial patterns. The limit point of the bifurcating solution is captured accurately by extending the analysis to the fifth order for the case of subcritical bifurcation. The numerical solutions are in good agreement with the predictions of the weakly nonlinear analysis for supercritical bifurcations. We show that when multiple steady states arise Turing patterns can coexist with another spatially uniform steady states. Furthermore, we show that our framework can be extended to get different patterns like squares and hexagons in a two-dimensional domain. We show that the shape of Turing patterns is influenced by the domain size. This shows that the geometry can influence the kind of patterns formed in natural systems. This study will aid the experimentalist identify operating conditions where Turing patterns can be obtained.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Deepak Kumar, Subramaniam Pushpavanam. 2024-07-03. A framework to identify supercritical and subcritical Turing bifurcations: Case study of a system sustaining cubic and quadratic autocatalysis. https://arxiv.org/abs/2403.17386

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

KEEP EXPLORING

Related papers

Self-Organization to the Edge of Ergodicity Breaking in a Complex Adaptive System

Self-organized criticality is widely invoked for collective behavior, yet its role in objective-driven, heterogeneous adaptive systems is unclear. We introduce {\tt EvoSK}: agents learn on a Sherrington--Kirkpatrick landscape while the least fit are replaced. It self-organizes to the edge of ergodicity breaking, with scale-free avalanches ($τ\approx -1.5$) and rewards beating any tuned non-evolutionary regime. Its cascade's branching ratio is the spectral radius of the learning dynamics' Jacobian, making critical branching and ergodicity breaking one marginal-stability condition fixing the exponent. The attraction to criticality follows from the selection--mutation balance: subcritical cascades decay too fast to dislodge frozen agents, supercritical cascades shield them from selection; only the critical power-law tail supplies the polynomial rate the balance requires.

nlin.AO

When higher-order interactions enhance synchronization: the case of the Kuramoto model

Synchronization is a fundamental phenomenon in complex systems, observed across a wide range of natural and engineered contexts. The Kuramoto model provides a foundational framework for understanding synchronization among coupled oscillators, traditionally assuming pairwise interactions. However, many real-world systems exhibit group and many-body interactions, which can be effectively modeled through hypergraphs. Here we show that the effect of such higher-order interactions on synchronization is non-monotonic. Through a numerical study of higher-order Kuramoto models on random hypergraphs and on globally coupled systems, we find that the degree of synchronization reached from incoherent initial conditions is maximized at a small but nonzero higher-order coupling strength: weak higher-order interactions enhance synchronization when added to pairwise ones, whereas strong ones work against it, in line with earlier reports of reduced basins and of cluster states. We further show, through a cost-constrained allocation analysis, that under a constrained budget for interactions a mixed allocation of pairwise and higher-order couplings consistently achieves higher synchronization than relying on either type alone. These findings clarify the role of higher-order interactions in shaping collective dynamics and point to design principles for optimizing synchronization in complex systems.

nlin.AO

Dynamics-preserving network reductions for ride-pooling paths

Reducing the complexity of ride-pooling paths is a central challenge in systems with distributed demand. Here we show that such dynamics admit an exact coarse-grained representation: for a broad class of routing algorithms whose decisions depend only on path lengths, the full network can be reduced to an effective network of active nodes weighted by shortest-path distances without altering the resulting trajectories, up to stochastic degeneracy breaking. The reduction therefore defines an equivalence class of network representations generating identical path dynamics. We further demonstrate that for globally optimizing dispatchers this equivalence is systematically violated through degeneracy amplification, yet remains quantitatively accurate beyond the exactly solvable regime. Our results identify when spatial structure can be integrated out without loss of dynamical fidelity, providing a general framework for the analysis of interacting path processes.

nlin.AO