The annealed Calderon-Zygmund estimate as convenient tool in quantitative stochastic homogenization
Marc Josien, Felix Otto

TL;DR
This paper uses annealed Calderon-Zygmund estimates to derive optimal gradient error bounds in quantitative stochastic homogenization of elliptic equations, providing a new proof avoiding quenched regularity theory.
Contribution
It introduces a novel proof of annealed CZ estimates relying solely on functional analysis, simplifying the approach in stochastic homogenization.
Findings
Optimal error estimates in homogenization error are obtained.
A new proof of annealed CZ estimates avoiding quenched regularity theory.
Estimates are valid for ensembles of Gaussian-related coefficient fields.
Abstract
This article is about the quantitative homogenization theory of linear elliptic equations in divergence form with random coefficients. We derive gradient estimates on the homogenization error, i.e. on the difference between the actual solution and the two-scale expansion of the homogenized solution, both in terms of strong norms (oscillation) and weak norms (fluctuation). These estimates are optimal in terms of scaling in the ratio between the microscopic and the macroscopic scale. The purpose of this article is to highlight the usage of the recently introduced annealed Calderon-Zygmund (CZ) estimates in obtaining the above, previously known, error estimates. Moreover, the article provides a novel proof of these annealed CZ estimate that completely avoids quenched regularity theory, but rather relies on functional analysis. It is based on the observation that even on the level of…
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