Numerical Continuation and SPDE Stability for the 2D Cubic-Quintic Allen-Cahn Equation

TL;DR

This paper extends numerical continuation methods to stochastic PDEs, specifically the 2D cubic-quintic Allen-Cahn equation, enabling analysis of stochastic fluctuations and bifurcations with practical computational efficiency.

Contribution

It introduces a novel approach for continuing covariance matrices along bifurcation branches in SPDEs, demonstrating quantification of stochastic fluctuations and scaling laws.

Findings

Quantified stochastic fluctuations along bifurcation branches.

Demonstrated scaling laws near bifurcation points.

Achieved efficient computation on standard desktop setups.

Abstract

We study the Allen-Cahn equation with a cubic-quintic nonlinear term and a stochastic $Q$-trace-class stochastic forcing in two spatial dimensions. This stochastic partial differential equation (SPDE) is used as a test case to understand, how numerical continuation methods can be carried over to the SPDE setting. First, we compute the deterministic bifurcation diagram for the PDE, i.e. without stochastic forcing. In this case, two locally asymptotically stable steady state solution branches exist upon variation of the linear damping term. Then we consider the Lyapunov operator equation for the locally linearized system around steady states for the SPDE. We discretize the full SPDE using a combination of finite-differences and spectral noise approximation obtaining a finite-dimensional system of stochastic ordinary differential equations (SODEs). The large system of SODEs is used to approximate the Lyapunov operator equation via covariance matrices. The covariance matrices are numerically continued along the two bifurcation branches. We show that we can quantify the stochastic fluctuations along the branches. We also demonstrate scaling laws near branch and fold bifurcation points. Furthermore, we perform computational tests to show that, even with a sub-optimal computational setup, we can quantify the subexponential-timescale fluctuations near the deterministic steady states upon stochastic forcing on a standard desktop computer setup. Hence, the proposed method for numerical continuation of SPDEs has the potential to allow for rapid parametric uncertainty quantification of spatio-temporal stochastic systems.