KPZ equation, its renormalization and invariant measures

TL;DR

This paper introduces a new regularization approach for the KPZ equation that facilitates the study of its invariant measures, leading to the identification of a specific invariant distribution involving geometric Brownian motion.

Contribution

It proposes a novel regularization method for the KPZ equation suitable for analyzing invariant measures, and demonstrates the invariance of a geometric Brownian motion distribution under the associated SHE.

Findings

The new regularization enables studying invariant measures of KPZ.

The limit equation is a linear SHE with an added linear term of coefficient 1/24.

The distribution of two-sided geometric Brownian motion is invariant under the SHE evolution.

Abstract

The Kardar-Parisi-Zhang (KPZ) equation is a stochastic partial differential equation which is ill-posed because the nonlinearity is marginally defined with respect to the roughness of the forcing noise. However, its Cole-Hopf solution, defined as the logarithm of the solution of the linear stochastic heat equation (SHE) with a multiplicative noise, is a mathematically well-defined object. In fact, Hairer [13] has recently proved that the solution of SHE can actually be derived through the Cole-Hopf transform of the solution of the KPZ equation with a suitable renormalization under periodic boundary conditions. This transformation is unfortunately not well adapted to studying the invariant measures of these Markov processes. The present paper introduces a different type of regularization for the KPZ equation on the whole line $\mathbb{R}$ or under periodic boundary conditions, which is appropriate from the viewpoint of studying the invariant measures. The Cole-Hopf transform applied to this equation leads to an SHE with a smeared noise having an extra complicated nonlinear term. Under time average and in the stationary regime, it is shown that this term can be replaced by a simple linear term, so that the limit equation is the linear SHE with an extra linear term with coefficient 1/24. The methods are essentially stochastic analytic: The Wiener-It\^o expansion and a similar method for establishing the Boltzmann-Gibbs principle are used. As a result, it is shown that the distribution of a two-sided geometric Brownian motion with a height shift given by Lebesgue measure is invariant under the evolution determined by the SHE on $\mathbb{R}$.