Beyond-all-order asymptotics for homoclinic snaking of localised patterns in reaction-transport systems

Abstract

Abstract Spatially localised stationary patterns of arbitrarily wide spatial extent emerge from subcritical Turing bifurcations in one-dimensional reaction-diffusion systems. They lie on characteristic bifurcation curves that oscillate around a Maxwell point as part of a homoclinic snaking phenomenon. Here, a generalisation of the exponential asymptotics method by Chapman & Kozyreff is developed to provide leading-order expressions for the width of the snaking region close to a Turing bifurcation’s super/sub critical transition in arbitrary n -component reaction-diffusion systems. First, general expressions are provided for the regular asymptotic approximation of the Maxwell point, which depends algebraically on the parametric distance from the codimension-two super/subcritical Turing bifurcation. Then, expressions are derived for the width of the snaking region, which is exponentially small in the same distance. The general theory is developed using a vectorised form of regular and exponential asymptotic expansions for localised patterns, which are matched at a Stokes line. Detailed calculations for a particular example are algebraically cumbersome, depend sensitively on the form of the reaction kinetics, and rely on the summation of a weakly convergent series obtained via optimal truncation. Nevertheless, the process can be automated, and code is provided that carries out the calculations automatically, only requiring the user to input the model and the values of parameters at a codimension-two bifurcation point. The general theory also applies to higher-order equations, including those that can be recast as a reaction-diffusion system. The theory is illustrated by comparing numerical computations of localised patterns in two versions of the Swift–Hohenberg equation with different nonlinearities, and versions of some classic activator-inhibitor reaction-diffusion systems.

The authors' abstract, as published at the source. Nonlinearity, 2026 · DOI ↗