论文标题

随机波方程的高阶近似

Higher order approximation for stochastic wave equation

论文作者

Liu, Xing, Deng, Weihua

论文摘要

通过从属杀死的布朗尼运动获得的过程的无限发电机(分数拉普拉斯)捕获了波传播的幂律衰减。本文研究了用分数laplacian作为空间操作员的随机波方程的数值方案,其噪声项是无限的尺寸布朗尼运动或分数布朗尼运动(FBM)。首先,我们建立了随机分数方程的轻度溶液的规律性。然后使用光谱盖金方法进行空间近似,并通过在无限的尺寸高斯噪声后提高空间收敛速率。在时间方向上,当温和解决方案的时间衍生物以于点的$ l^p $ -norm的意义界定时,我们提出了一种改进的随机三角法,获得的强大收敛速率比现有结果更高,即时间融合率大于$ 1 $。特别是,对于时间离散,提供的方法可以按需要一些额外的规律性来达到$ 2 $的订单。理论误差估计值通过数值实验证实。

The infinitesimal generator (fractional Laplacian) of a process obtained by subordinating a killed Brownian motion catches the power-law attenuation of wave propagation. This paper studies the numerical schemes for the stochastic wave equation with fractional Laplacian as the space operator, the noise term of which is an infinite dimensional Brownian motion or fractional Brownian motion (fBm). Firstly, we establish the regularity of the mild solution of the stochastic fractional wave equation. Then a spectral Galerkin method is used for the approximation in space, and the space convergence rate is improved by postprocessing the infinite dimensional Gaussian noise. In the temporal direction, when the time derivative of the mild solution is bounded in the sense of mean-squared $L^p$-norm, we propose a modified stochastic trigonometric method, getting a higher strong convergence rate than the existing results, i.e., the time convergence rate is bigger than $1$. Particularly, for time discretization, the provided method can achieve an order of $2$ at the expenses of requiring some extra regularity to the mild solution. The theoretical error estimates are confirmed by numerical experiments.

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