Random Surfaces
Scott Sheffield
Abstract
We study "random surfaces," which are random real (or integer) valued functions on Zd. The laws are determined by convex, nearest neighbor, difference potentials that are invariant under translation by a full-rank sublattice L of Zd; they include many discrete and continuous height models (e.g., domino tilings, square ice, the harmonic crystal, the Ginzburg-Landau grad-phi interface model, the linear solid-on-solid model) as special cases. A gradient phase is an L-ergodic gradient Gibbs measure with finite specific free energy. A gradient phase is smooth if it is the gradient of an ordinary Gibbs measure; otherwise it is rough. We prove a variational principle--characterizing gradient phases of a given slope as minimizers of the specific free energy--and an empirical measure large deviations principle (with a unique rate function minimizer) for random surfaces on mesh approximations of bounded domains. Using a geometric technique called "cluster swapping" (a variant of the Swendsen-Wang update for Fortuin-Kasteleyn clusters), we also prove that the surface tension is strictly convex and that if u is in the interior of the space of finite-surface tension slopes, then there exists a minimal energy gradient phase muu of slope u. This muu is always unique for real valued random surfaces. In the discrete models, muu is unique if at least one of the following holds: d is in 1, 2, there exists a rough gradient phase of slope u, or u is irrational. When d=2, the slopes of all smooth phases (a.k.a. crystal facets) lie in the dual lattice of L.
Create a lesson
Related papers
Distribution-constrained optimal multiple stopping: the Root-type solution
Shuoqing Deng, Daxin Huang
Universality and sharp thresholds for ellipsoid fitting
Frederic Koehler, Youngtak Sohn
Local Laws and Edge Universality for Noncentral Sample Covariance Matrices
Can Hu, Jiang Hu, Zhidong Bai
Well-posedness and regularity of stochastic heat equations on moving domains
Chongyang Ren, Tusheng Zhang
Traveling Waves in Equity Markets with Rank-Based Entry and Exit
Graeme Baker, Caroline Smyth
An approximate zero bias transformation for random sums: Applications to sampling with outliers, auto insurance, and generative AI
Wasamon Jantai, Nathakhun Wiroonsri