Computing Transition Rates for the 1-D Stochastic Ginzburg–Landau–Allen–Cahn Equation for Finite-Amplitude Noise with a Rare Event Algorithm View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-01

AUTHORS

Joran Rolland, Freddy Bouchet, Eric Simonnet

ABSTRACT

In this article 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 for one degree of freedom systems. Moreover, the mean duration growths logarithmically with the inverse temperature. The prefactor given by the potential curvature grows exponentially with size. The main novelty of our work is that we also perform an analysis of reactive trajectories for large noises and large domains. In this case, we show that the position of the reactive front is essentially a random walk. This time, the mean duration grows linearly with the inverse temperature and quadratically with the size. Using a phenomenological description of the system, we are able to calculate the transition rate, although the dynamics is described by neither Freidlin–Wentzell or Eyring–Kramers type of results. Numerical results confirm our analysis. More... »

PAGES

277-311

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10955-015-1417-4

DOI

http://dx.doi.org/10.1007/s10955-015-1417-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049075232


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