Random conductance models with stable-like jumps: Quenched invariance principle

Xin Chen, Takashi Kumagai, Jian Wang

Research output: Contribution to journalArticlepeer-review

Abstract

We study the quenched invariance principle for random conductance models with long range jumps on Zd, where the transition probability from x to y is, on average, comparable to |x − y|(d+α) with α ∈ (0, 2) but is allowed to be degenerate. Under some moment conditions on the conductance, we prove that the scaling limit of the Markov process is a symmetric α-stable Lévy process on Rd. The well-known corrector method in homogenization theory does not seem to work in this setting. Instead, we utilize probabilistic potential theory for the corresponding jump processes. Two essential ingredients of our proof are the tightness estimate and the Hölder regularity of caloric functions for nonelliptic α-stable-like processes on graphs. Our method is robust enough to apply not only for Zd but also for more general graphs whose scaling limits are nice metric measure spaces.

Original languageEnglish
Pages (from-to)1180-1231
Number of pages52
JournalAnnals of Applied Probability
Volume31
Issue number3
DOIs
Publication statusPublished - 2021 Jun
Externally publishedYes

Keywords

  • Long range jump
  • Quenched invariance principle
  • Random conductance model
  • Stable-like process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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