Analysis of the gradient for the stochastic fractional heat equation with spatially-colored noise in $\mathbb R^d$

TL;DR

This paper investigates the spatial gradient behavior of solutions to a stochastic fractional heat equation driven by colored noise, deriving laws of iterated logarithm and q-variation properties.

Contribution

It provides a detailed analysis of the spatial gradient of solutions to a stochastic fractional heat equation with colored noise, including asymptotic behavior and variation properties.

Findings

Derived the law of iterated logarithm for the spatial gradient.

Characterized the q-variations of the solution in space.

Analyzed the asymptotic behavior of the spatial gradient as epsilon approaches zero.

Abstract

Consider the stochastic partial differential equation $$ \frac{\partial }{\partial t}u_t(\mathbf{x})= -(-\Delta)^{\frac{\alpha}{2}}u_t(\mathbf{x}) +b\left(u_t(\mathbf{x})\right)+\sigma\left(u_t(\mathbf{x})\right) \dot F(t, \mathbf{x}), \ \ \ t\ge0, \mathbf{x}\in \mathbb R^d, $$ where $-(-\Delta)^{\frac{\alpha}{2}}$ denotes the fractional Laplacian with the power $\alpha/2\in (1/2,1]$, and the driving noise $\dot F$ is a centered Gaussian field which is white in time and with a spatial homogeneous covariance given by the Riesz kernel. We study the detailed behavior of the approximation spatial gradient $u_t(\mathbf{x})-u_t(\mathbf{x}-\varepsilon \mathbf e)$ at any fixed time $t>0$, as $\varepsilon\downarrow 0$, where $\mathbf e$ is the unit vector in $\mathbb R^d$. As applications, we deduce the law of iterated logarithm and the behavior of the $q$-variations of the solution in space.