# Quantitative stochastic homogenization and regularity theory of parabolic equations

## TL;DR

This paper establishes a quantitative framework for stochastic homogenization of parabolic equations, providing optimal error estimates and higher regularity results by analyzing subadditive quantities and employing a renormalization scheme.

## Contribution

It introduces a novel quantitative approach to stochastic homogenization for parabolic equations, extending elliptic techniques and achieving optimal error bounds and regularity results.

## Key findings

- Derived algebraic convergence rates for subadditive quantities.
- Obtained optimal homogenization error estimates in stochastic integrability.
- Developed higher regularity results including uniform Lipschitz estimates and Liouville theorems.

## Abstract

We develop a quantitative theory of stochastic homogenization for linear, uniformly parabolic equations with coefficients depending on space and time. Inspired by recent works in the elliptic setting, our analysis is focused on certain subadditive quantities derived from a variational interpretation of parabolic equations. These subadditive quantities are intimately connected to spatial averages of the fluxes and gradients of solutions. We implement a renormalization-type scheme to obtain an algebraic rate for their convergence, which is essentially a quantification of the weak convergence of the gradients and fluxes of solutions to their homogenized limits. As a consequence, we obtain estimates of the homogenization error for the Cauchy-Dirichlet problem which are optimal in stochastic integrability. We also develop a higher regularity theory for solutions of the heterogeneous equation, including a uniform $C^{0,1}$-type estimate and a Liouville theorem of every finite order.