Optimal corrector estimates on percolation clusters
Probability
2020-05-15 v2 Analysis of PDEs
Abstract
We prove optimal quantitative estimates on the first-order correctors on supercritical percolation clusters: we show that they are bounded in and have logarithmic growth in , in the sense of stretched exponential moments. The main ingredients are a renormalization scheme of the supercritical percolation cluster, following the works of Pisztora and Barlow; large-scale regularity estimates developed in the previous paper; and a nonlinear concentration inequality of Efron-Stein type which is used to transfer quantitative information from the environment to the correctors.
Keywords
Cite
@article{arxiv.1805.00902,
title = {Optimal corrector estimates on percolation clusters},
author = {Paul Dario},
journal= {arXiv preprint arXiv:1805.00902},
year = {2020}
}
Comments
51 pages; revised version, accepted for publication in Annals of Applied Probability