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Large deviations principle for invariant measures of stochastic Burgers equations by Bai, Rui; Feng, Chunrong; Zhao, Huaizhong is a scholarly article available to read on EtoBox.

What is Large deviations principle for invariant measures of stochastic Burgers equations about?

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 $\sqrt{\epsilon} \rightarrow 0$ and the covariance operator $Q_\epsilon$ is convergent to $(-\Delta)^{\frac 1 2}$ and prove a large deviations principle for solutions, uniformly with respect to the initial value of equation. Furthermore, we set $Q_\epsilon$ to be a trace class operator and converge to $(-\Delta)^{\frac{\alpha}{2}}$ with $\alpha<1$ 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.

Author

Bai, Rui; Feng, Chunrong; Zhao, Huaizhong

Published

2024

Language

EN