Approximate Lie symmetries and singular perturbation theory

TL;DR

This paper introduces a symmetry-based reformulation of singular perturbation theory using Lie symmetries, providing a geometric perspective that simplifies the construction of uniformly convergent solutions and clarifies the success and failure of traditional methods.

Contribution

It develops an approach to singular perturbation problems via approximate Lie symmetries, unifying existing methods and enabling easier, more systematic solution construction.

Findings

Approximate symmetries are straightforward to compute and non-singular.

The method subsumes RG, multiple scales, and Poincare-Lindstedt techniques.

It offers a systematic way to identify when perturbation methods succeed or fail.

Abstract

Singular perturbation theory plays a central role in the approximate solution of nonlinear differential equations. However, applying these methods is a subtle art owing to the lack of globally applicable algorithms. Inspired by the fact that all exact solutions of differential equations are consequences of (Lie) symmetries, we reformulate perturbation theory for differential equations in terms of expansions of the Lie symmetries of the solutions. This is a change in perspective from the usual method of obtaining series expansions of the solutions themselves. We show that these approximate symmetries are straightforward to calculate and are never singular; their integration is therefore an easier way of constructing uniformly convergent solutions. This geometric viewpoint naturally subsumes the RG-inspired approach of Chen, Goldenfeld and Oono, the method of multiple scales, and the Poincare-Lindstedt method, by exploiting a fundamental class of symmetries that we term ``hidden scale symmetries''. It also clarifies when and why these singular perturbation methods succeed and just as importantly, when they fail. More broadly, direct, algorithmic identification and integration of these hidden scale symmetries permits solution of problems where other methods are impractical.