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arXiv 2408.00951math.NAcs.NAmath.PR
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关于具有分数型噪声的随机Burgers方程的全离散方案的强收敛性

Strong convergence of a fully discrete scheme for stochastic Burgers equation with fractional-type noise

School of Mathematics, Southeast University(东南大学数学学院)

Yibo Wang, Wanrong Cao

AI总结:

本文研究了由加性圆柱分数布朗运动驱动的随机Burgers方程的数值近似,采用谱伽辽金方法空间离散,结合非线性 tame 加速指数欧拉方法构建全离散方案,通过证明分数布朗运动的随机卷积指数可积性,得出半离散和全离散近似的矩有界性,并利用停止时间技术证明方案的强收敛性。

摘要:

我们研究了由加性圆柱分数布朗运动驱动的随机Burgers方程的数值近似。为了对连续问题进行空间离散,采用谱伽辽金方法,随后提出非线性 tame 加速指数欧拉方法以得到全离散方案。通过证明分数布朗运动的随机卷积的指数可积性,我们展示了半离散和全离散近似的矩有界性。基于这些结果以及通过停止时间技术证明的全离散方案在概率意义下的收敛性,我们推导了所提出方案的强收敛性。

英文摘要:

We investigate numerical approximations for the stochastic Burgers equation driven by an additive cylindrical fractional Brownian motion with Hurst parameter $H \in (\frac{1}{2}, 1)$. To discretize the continuous problem in space, a spectral Galerkin method is employed, followed by the presentation of a nonlinear-tamed accelerated exponential Euler method to yield a fully discrete scheme. By showing the exponential integrability of the stochastic convolution of the fractional Brownian motion, we present the boundedness of moments of semi-discrete and full-discrete approximations. Building upon these results and the convergence of the fully discrete scheme in probability proved by a stopping time technique, we derive the strong convergence of the proposed scheme.

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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