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arXiv 2402.16622math.PRmath.AP
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随机演化方程在临界变分设定下的大偏差分析

Large Deviations for Stochastic Evolution Equations in the Critical Variational Setting

Delft University of Technology(代尔夫特理工大学)

Esmée Theewis, Mark Veraar

AI总结:

本文在临界变分设定下,利用弱收敛方法证明了具有小高斯噪声的拟线性随机演化方程解的大偏差原理,并解决了长期未解决的开放问题。

摘要:

利用弱收敛方法,我们证明了对于具有小高斯噪声的拟线性随机演化方程解的大偏差原理(LDP)在临界变分设定下成立,这是一种 recently developed general variational framework。除了为well-posedness所需的假设外,没有做出其他假设。特别是,不需要单调性,也不需要Gelfand三元组中的紧嵌入。此外,我们允许扩散系数具有灵活的增长,包括梯度噪声。这导致了众多应用,其中LDP尚未建立,特别是对于在无界域上的方程。由于我们的框架包括带有梯度噪声和无界域的2D纳维-斯托克斯和布西尼奇方程,我们的结果解决了一个已有超过15年未解决的开放问题。

英文摘要:

Using the weak convergence approach, we prove the large deviation principle (LDP) for solutions to quasilinear stochastic evolution equations with small Gaussian noise in the critical variational setting, a recently developed general variational framework. No additional assumptions are made apart from those required for well-posedness. In particular, no monotonicity is required, nor a compact embedding in the Gelfand triple. Moreover, we allow for flexible growth of the diffusion coefficient, including gradient noise. This leads to numerous applications for which the LDP was not established yet, in particular equations on unbounded domains with gradient noise. Since our framework includes the 2D Navier-Stokes and Boussinesq equations with gradient noise and unbounded domains, our results resolve an open problem that has remained unsolved for over 15 years.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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