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arXiv 1802.04844math.PR
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Ito随机微分方程基于统一Taylor-Ito和Taylor-Stratonovich展开的2.0和2.5阶显式一步强数值方法

Explicit One-Step Strong Numerical Methods of Orders 2.0 and 2.5 for Ito Stochastic Differential Equations Based on the Unified Taylor-Ito and Taylor-Stratonovich Expansions

Dmitriy F. Kuznetsov

摘要:

本文研究了带多维非交换噪声的Ito随机微分方程的显式一步强数值方法,构造了收敛阶为2.0和2.5的显式一步强数值方法.我们考虑基于统一的Taylor-Ito和Taylor-Stratonovich展开式的数值方法。对于重数为1 ~ 5的Ito和Stratonovich迭代随机积分的数值模拟,我们应用了在希尔伯特空间$L_2([t, T]^k),$ $k=1,\ldots,5$中在范数意义下收敛的多重Fourier-Legendre级数方法。这篇文章是写给工程师谁使用数字建模在随机控制和解决非线性滤波问题。这篇文章对于从事随机微分方程数值积分研究的科学家来说是很有意义的。

英文摘要:

The article is devoted to the construction of explicit one-step strong numerical methods with the orders 2.0 and 2.5 of convergence for Ito stochastic differential equations with multidimensional non-commutative noise. We consider the numerical methods based on the unified Taylor-Ito and Taylor-Stratonovich expansions. For the numerical modeling of iterated Ito and Stratonovich stochastic integrals of multiplicities 1 to 5 we apply the method of multiple Fourier-Legendre series converging in the sense of norm in Hilbert space $L_2([t, T]^k),$ $k=1,\ldots,5$. The article is addressed to engineers who use numerical modeling in stochastic control and for solving the non-linear filtering problem. The article will be interesting to scientists who working in the field of numerical integration of stochastic differential equations.

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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