Large deviations principle for invariant measures of stochastic Burgers equations

Fuente: arXiv
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Main Authors: Bai, Rui, Feng, Chunrong, Zhao, Huaizhong
Format: Preprint
Published: 2024
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author Bai, Rui
Feng, Chunrong
Zhao, Huaizhong
author_facet Bai, Rui
Feng, Chunrong
Zhao, Huaizhong
contents We study the small noise asymptotic for stochastic Burgers equations on $(0,1)$ with Dirichlet boundary condition. We consider the case that the noise is more singular than space-time white noise. We let the noise magnitude $\sqrtε \rightarrow 0$ and the covariance operator $Q_ε$ is convergent to $(-Δ)^{\frac 1 2}$ and prove a large deviations principle for solutions, uniformly with respect to the initial value of equation. Furthermore, we set $Q_ε$ to be a trace class operator and converge to $(-Δ)^{\fracα{2}}$ with $α<1$ in a suitable way such that the invariant measures exist. Then, we prove the large deviations principle for the invariant measures of stochastic Burgers equations.
format Preprint
id arxiv_https___arxiv_org_abs_2409_14234
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Large deviations principle for invariant measures of stochastic Burgers equations
Bai, Rui
Feng, Chunrong
Zhao, Huaizhong
Probability
Analysis of PDEs
Primary 60H10, 60B10, secondary 37A50
We study the small noise asymptotic for stochastic Burgers equations on $(0,1)$ with Dirichlet boundary condition. We consider the case that the noise is more singular than space-time white noise. We let the noise magnitude $\sqrtε \rightarrow 0$ and the covariance operator $Q_ε$ is convergent to $(-Δ)^{\frac 1 2}$ and prove a large deviations principle for solutions, uniformly with respect to the initial value of equation. Furthermore, we set $Q_ε$ to be a trace class operator and converge to $(-Δ)^{\fracα{2}}$ with $α<1$ in a suitable way such that the invariant measures exist. Then, we prove the large deviations principle for the invariant measures of stochastic Burgers equations.
title Large deviations principle for invariant measures of stochastic Burgers equations
topic Probability
Analysis of PDEs
Primary 60H10, 60B10, secondary 37A50
url https://arxiv.org/abs/2409.14234