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arXiv 1801.09348math.NAcs.NAmath.PR
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带白噪声的随机Allen-Cahn方程的强逼近

Strong Approximation of Stochastic Allen-Cahn Equation with White Noise

Zhihui Liu, Zhonghua Qiao

摘要:

建立了在加性时空白噪声驱动下二阶抛物型随机偏微分方程(包括随机Allen-Cahn方程)的全离散数值格式的最优强收敛速度.首先将原随机方程转化为一个等价的随机方程,使其解比原随机方程具有更强的正则性。然后,我们使用向后欧拉在时间和空间谱伽辽金完全离散这个随机方程。在单调性假设下,结合鞅型2巴拿赫空间中的因子分解方法和随机分析,得到了随机方程和随机方程的一致最大模估计和Hölder正则性.最后,在$l_t^\infty L^2_ωL^2_x \cap l_t^q L^q_ωL^q_x$ -范数下证明了所提出的全离散格式的强收敛速度.数值实验验证了理论结果。

英文摘要:

We establish an optimal strong convergence rate of a fully discrete numerical scheme for second order parabolic stochastic partial differential equations with monotone drifts, including the stochastic Allen-Cahn equation, driven by an additive space-time white noise. Our first step is to transform the original stochastic equation into an equivalent random equation whose solution possesses more regularity than the original one. Then we use the backward Euler in time and spectral Galerkin in space to fully discretize this random equation. By the monotone assumption, in combination with the factorization method and stochastic calculus in martingale-type 2 Banach spaces, we derive a uniform maximum norm estimation and a Hölder-type regularity for both stochastic and random equations. Finally, the strong convergence rate of the proposed fully discrete scheme under the $l_t^\infty L^2_ωL^2_x \cap l_t^q L^q_ωL^q_x$-norm is obtained. Several numerical experiments are carried out to verify the theoretical result.

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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