Uniqueness of gradient Gibbs measures with disorder

TL;DR

This paper proves the existence and uniqueness of shift-covariant gradient Gibbs measures with a given tilt in disordered models, extending previous results to higher dimensions and more general disorder structures.

Contribution

It establishes the conditions for existence and uniqueness of gradient Gibbs measures with disorder, including ergodicity and decay of covariances, in new settings.

Findings

Existence and uniqueness of measures in d≥3 for i.i.d. symmetric disorder.

Existence and uniqueness in all dimensions for stationary disorder.

Optimal decay rates of covariances under Poincaré inequality conditions.

Abstract

We consider - in uniformly strictly convex potential regime - two versions of random gradient models with disorder. In model (A) the interface feels a bulk term of random fields while in model (B) the disorder enters though the potential acting on the gradients. We assume a general distribution on the disorder with uniformly-bounded finite second moments. It is well known that for gradient models without disorder there are no Gibbs measures in infinite-volume in dimension $d = 2$, while there are shift-invariant gradient Gibbs measures describing an infinite-volume distribution for the gradients of the field, as was shown by Funaki and Spohn. Van Enter and Kuelske proved in 2008 that adding a disorder term as in model (A) prohibits the existence of such gradient Gibbs measures for general interaction potentials in $d = 2$. In Cotar and Kuelske (2012) we proved the existence of shift-covariant random gradient Gibbs measures for model (A) when $d\geq 3$, the disorder is i.i.d and has mean zero, and for model (B) when $d\geq 1$ and the disorder has stationary distribution. In the present paper, we prove existence and uniqueness of shift-covariant random gradient Gibbs measures with a given expected tilt $u\in R^d$ and with the corresponding annealed measure being ergodic: for model (A) when $d\geq 3$ and the disordered random fields are i.i.d. and symmetrically-distributed, and for model (B) when $d\geq 1$ and for any stationary disorder dependence structure. We also compute for both models for any gradient Gibbs measure constructed as in Cotar and Kuelske (2012), when the disorder is i.i.d. and its distribution satisfies a Poincar\'e inequality assumption, the optimal decay of covariances with respect to the averaged-over-the-disorder gradient Gibbs measure.