Abstract

We study the realized power variations for the fourth order linearized Kuramoto–Sivashinsky (LKS) SPDEs and their gradient, driven by the space–time white noise in one-to-three dimensional spaces, in time, have infinite quadratic variation and dimension-dependent Gaussian asymptotic distributions. This class was introduced-with Brownian-time-type kernel formulations by Allouba in a series of articles starting in 2006. He proved the existence, uniqueness, and sharp spatio-temporal Hölder regularity for the above class of equations in d=1,2,3. We use the relationship between LKS-SPDEs and the Houdré–Villaa bifractional Brownian motion (BBM), yielding temporal central limit theorems for LKS-SPDEs and their gradient. We use the underlying explicit kernels and spectral/harmonic analysis to prove our results. On one hand, this work builds on the recent works on the delicate analysis of variations of general Gaussian processes and stochastic heat equation driven by the space–time white noise. On the other hand, it builds on and complements Allouba’s earlier works on the LKS-SPDEs and their gradient.

Highlights

  • SPDEs in One-to-Three Dimensions.The fourth order linearized Kuramoto–Sivashinsky (LKS) SPDEs are related to the model of pattern formation phenomena accompanying the appearance of turbulence.The fundamental kernel associated with the deterministic version of this class is built on the Brownian-time process in [3,7,8]

  • In this paper we show that the realized power variation of the process U and its gradient in time, have infinite quadratic variation and dimension-dependent Gaussian asymptotic distributions

  • We have presented that the realized power variations for the fourth order

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Summary

Introduction

SPDEs in One-to-Three Dimensions.The fourth order linearized Kuramoto–Sivashinsky (LKS) SPDEs are related to the model of pattern formation phenomena accompanying the appearance of turbulence (see [1,2,3,4] for the LKS class and for its connection to many classical and new examples of deterministic and stochastic pattern formation PDEs, and see [5,6] for classical examples of deterministic and stochastic pattern formation PDEs).The fundamental kernel associated with the deterministic version of this class is built on the Brownian-time process in [3,7,8]. We give exact dimension-dependent asymptotic distributions of the realized power variations in time, for the important class of stochastic equation: ∂U ε ∂ d +1 W

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