arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.01474math.PRcs.NAmath.NA
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具有乘性噪声的半线性随机微分方程随机周期解的一阶显式逼近

Order-one explicit approximations of random periodic solutions of semi-linear SDEs with multiplicative noise

Yujia Guo, Xiaojie Wang, Yue Wu

AI总结:

本文针对具有非全局 Lipschitz 系数和乘性噪声的半线性随机微分方程,提出一阶显式格式逼近其随机周期解,并证明其在无限时间范围内具有一阶均方收敛性。

摘要:

本文致力于研究由乘性噪声驱动且具有非全局 Lipschitz 系数的随机微分方程(SDEs)随机周期解的一阶显式逼近。随机周期解的存在性通过离散化 SDE 的拉回(pull-back)极限得到证明。本文引入了一种新的方法来分析所提格式的均方误差界,该方法不依赖于数值逼近的先验高阶矩界。在温和假设下,证明了所提格式在无限时间范围内能够达到期望的一阶均方收敛。最后提供了数值算例以验证理论结果。

英文摘要:

This paper is devoted to order-one explicit approximations of random periodic solutions to multiplicative noise driven stochastic differential equations (SDEs) with non-globally Lipschitz coefficients. The existence of the random periodic solution is demonstrated as the limit of the pull-back of the discretized SDE. A novel approach is introduced to analyze mean-square error bounds of the proposed scheme that does not depend on a prior high-order moment bounds of the numerical approximations. Under mild assumptions, the proposed scheme is proved to achieve an expected order-one mean square convergence in the infinite time horizon. Numerical examples are finally provided to verify the theoretical results.

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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