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arXiv 2203.00571math.NAcs.NA
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全离散有限差分方法在随机Cahn-Hilliard方程中的密度收敛性

Density convergence of a fully discrete finite difference method for stochastic Cahn--Hilliard equation

Academy of Mathematics and Systems Science, Chinese Academy of Sciences(中国科学院数学与系统科学研究院);School of Mathematical Sciences, University of Chinese Academy of Sciences(中国科学院大学数学科学学院);School of Mathematics and Statistics, Huazhong University of Science and Technology(华中科技大学数学与统计学院)

Jialin Hong, Diancong Jin, Derui Sheng

AI总结:

本文提出一种新方法,解决随机Cahn-Hilliard方程全离散有限差分方法的密度收敛性问题,并部分回答了关于计算精确解密度的开放问题。

摘要:

本文聚焦于研究全离散有限差分方法在数值求解由乘性时空白噪声驱动的随机Cahn-Hilliard方程时的密度收敛性。主要困难在于漂移系数既不全局Lipschitz连续也不单侧Lipschitz连续。为解决此问题,我们提出了一种新的局部化论证方法,并推导了数值解的强收敛速率,以估计精确解与数值解之间的总变差距离。这加上数值解密度存在的结论,最终得出数值解在$ L^1(\mathbb{R}) $中的密度收敛性。我们的结果部分回答了[J. Cui和J. Hong, J. Differential Equations (2020)]中提出的关于计算精确解密度的开放问题。

英文摘要:

This paper focuses on investigating the density convergence of a fully discrete finite difference method when applied to numerically solve the stochastic Cahn--Hilliard equation driven by multiplicative space-time white noises. The main difficulty lies in the control of the drift coefficient that is neither globally Lipschitz nor one-sided Lipschitz. To handle this difficulty, we propose a novel localization argument and derive the strong convergence rate of the numerical solution to estimate the total variation distance between the exact and numerical solutions. This along with the existence of the density of the numerical solution finally yields the convergence of density in $L^1(\mathbb{R})$ of the numerical solution. Our results partially answer positively to the open problem emerged in [J. Cui and J. Hong, J. Differential Equations (2020)] on computing the density of the exact solution numerically.

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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