Quantitative homogenization and large-scale regularity of Poisson point clouds

Fuente: arXiv
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Main Authors: Armstrong, Scott, Venkatraman, Raghavendra
Format: Preprint
Published: 2023
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author Armstrong, Scott
Venkatraman, Raghavendra
author_facet Armstrong, Scott
Venkatraman, Raghavendra
contents We prove quantitative homogenization results for harmonic functions on supercritical continuum percolation clusters--that is, Poisson point clouds with edges connecting points which are closer than some fixed distance. We show that, on large scales, harmonic functions resemble harmonic functions in Euclidean space with sharp quantitative bounds on their difference. In particular, for every point cloud which is supercritical (meaning that the intensity of the Poisson process is larger than the critical parameter which guarantees the existence of an infinite connected component), we obtain optimal corrector bounds, homogenization error estimates and large-scale regularity results.
format Preprint
id arxiv_https___arxiv_org_abs_2309_12900
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Quantitative homogenization and large-scale regularity of Poisson point clouds
Armstrong, Scott
Venkatraman, Raghavendra
Probability
Analysis of PDEs
We prove quantitative homogenization results for harmonic functions on supercritical continuum percolation clusters--that is, Poisson point clouds with edges connecting points which are closer than some fixed distance. We show that, on large scales, harmonic functions resemble harmonic functions in Euclidean space with sharp quantitative bounds on their difference. In particular, for every point cloud which is supercritical (meaning that the intensity of the Poisson process is larger than the critical parameter which guarantees the existence of an infinite connected component), we obtain optimal corrector bounds, homogenization error estimates and large-scale regularity results.
title Quantitative homogenization and large-scale regularity of Poisson point clouds
topic Probability
Analysis of PDEs
url https://arxiv.org/abs/2309.12900