Computing transition rates for the 1-D stochastic Ginzburg--Landau--Allen--Cahn equation for finite-amplitude noise with a rare event algorithm
Joran Rolland (INLN), Freddy Bouchet (Phys-ENS), Eric Simonnet (INLN)

TL;DR
This paper computes transition rates and analyzes reactive trajectories of the 1-D stochastic Allen-Cahn equation under various noise regimes and domain sizes, using an efficient rare event algorithm and providing new insights into large-noise behavior.
Contribution
It introduces an analysis of reactive trajectories for large noise and domain sizes, extending beyond classical small-noise theories like Freidlin-Wentzell.
Findings
Reactive trajectory durations follow a Gumbel distribution.
Mean duration grows logarithmically with inverse temperature in small noise.
Reactive front position behaves as a random walk in large noise and domain size.
Abstract
In this paper we compute and analyse the transition rates and duration of reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude white noise, as well as for small and large domain. We demonstrate that extremely rare reactive trajectories corresponding to direct transitions between two metastable states are efficiently computed using an algorithm called adaptive multilevel splitting. This algorithm is dedicated to the computation of rare events and is able to provide ensembles of reactive trajectories in a very efficient way. In the small noise limit, our numerical results are in agreement with large-deviation predictions such as instanton-like solutions, mean first passages and escape probabilities. We show that the duration of reactive trajectories follows a Gumbel distribution like…
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