Average and deviation for slow-fast stochastic partial differential equations

TL;DR

This paper develops an averaging method for slow-fast stochastic partial differential equations without Lipschitz conditions, providing convergence rates and Gaussian deviation estimates, improving error approximations over previous methods.

Contribution

It introduces a novel averaging approach for SPDEs without Lipschitz assumptions, with explicit convergence and deviation results.

Findings

Derived an averaged equation for SPDEs without Lipschitz conditions.

Established convergence rate in probability for the averaging approximation.

Showed the deviation is Gaussian with errors of order $\\mathcal{O}(\e)$.

Abstract

Averaging is an important method to extract effective macroscopic dynamics from complex systems with slow modes and fast modes. This article derives an averaged equation for a class of stochastic partial differential equations without any Lipschitz assumption on the slow modes. The rate of convergence in probability is obtained as a byproduct. Importantly, the deviation between the original equation and the averaged equation is also studied. A martingale approach proves that the deviation is described by a Gaussian process. This gives an approximation to errors of $\mathcal{O}(\e)$ instead of $\mathcal{O}(\sqrt{\e})$ attained in previous averaging.