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Difference methods for stochastic space fractional diffusion equation driven by additive space-time white noise via Wong-Zakai approximation

机译:基于Wong-Zakai近似的加性时空白噪声驱动的随机空间分数级扩散方程的差分方法

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The fractional diffusion equations are needed to describe the diffusion process in an inhomogeneous medium; further incorporating the noise term in the fractional diffusion equations leads to stochastic fractional diffusion equation. In this work, our motivation is to design stochastic finite difference schemes for understanding the behavior of the solution of modified stochastic space fractional diffusion equations. Initially, the noise term is approximated by a piecewise constant random process that leads to a modified stochastic differential equation. We applied a shifted Grunwald-Letnikov formula (one shift) for discretizing the spatial derivative involved in the space fractional diffusion equation. The principle results of difference schemes, that is, consistency, stability, and convergence, are proved in a mean-square sense. Further, the accuracy and efficiency of the proposed schemes are investigated through numerical experiments.
机译:需要分数扩散方程来描述非均匀介质中的扩散过程;进一步将噪声项纳入分数扩散方程可得到随机分数扩散方程。在这项工作中,我们的动机是设计随机有限差分方案,以理解修正随机空间分数级扩散方程解的行为。最初,噪声项通过分段常数随机过程近似,该过程导致修正的随机微分方程。我们应用了移位的Grunwald-Letnikov公式(一个移位)来离散化空间分数级扩散方程中涉及的空间导数。在均方意义上证明了差分方案的主要结果,即一致性、稳定性和收敛性。此外,通过数值实验研究了所提方案的精度和效率。

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