Sample path large deviations for Laplacian models in $(1+1)$-dimensions

TL;DR

This paper establishes large deviation principles for Laplacian models in (1+1) dimensions, revealing complex minimiser structures linked to phase transitions influenced by pinning strength and boundary conditions.

Contribution

It provides the first detailed analysis of sample path large deviations for Laplacian models, highlighting richer minimiser structures compared to gradient systems.

Findings

Multiple minimisers depending on pinning strength and boundary conditions.

Rich phase structures linked to the minimiser configurations.

Extension of large deviation results to Laplacian models in (1+1) dimensions.

Abstract

For Laplacian models in dimension $(1+1)$ we derive sample path large deviations for the profile height function, that is, we study scaling limits of Gaussian integrated random walks and Gaussian integrated random walk bridges perturbed by an attractive force towards the zero-level, called pinning. We study in particular the regime when the rate functions of the corresponding large deviation principles admit more than one minimiser, in our models either two, three, or five minimiser depending on the pinning strength and the boundary conditions. This study complements corresponding large deviation results for gradient systems with pinning for Gaussian random walk bridges in $ (1+1) $-dimension (\cite{FS04}) and in $(1+d) $-dimension (\cite{BFO}), and recently in higher dimensions in \cite{BCF}. In particular it turns out that the Laplacian cases, i.e., integrated random walks, show richer and more complex structures of the minimiser of the rate functions which are linked to different phases.