一类随机李-泊松系统的李-泊松数值方法

IF 1.3 4区 数学 Q1 MATHEMATICS International Journal of Numerical Analysis and Modeling Pub Date : 2024-01-01 DOI:10.4208/ijnam2024-1004
Qianqian Liu, Lijin Wang
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引用次数: 0

摘要

我们针对一类随机李-泊松系统提出了一种基于李-泊松还原的数值方法。这类系统在与系统所在的李群流形相关的李代数的对偶 $\mathfrak{g}^∗$ 上被转化为 SDE,这也是通过动量映射在李群余切束上的随机哈密顿系统的还原形式。随机泊松积分器是通过将余切束上的随机交映方法离散地还原为 $\mathfrak{g}^∗ 上的积分器而得到的。随机刚体系统的应用说明了这一理论,并提供了方法的数值验证。
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Lie-Poisson Numerical Method for a Class of Stochastic Lie-Poisson Systems
We propose a numerical method based on the Lie-Poisson reduction for a class of stochastic Lie-Poisson systems. Such system is transformed to SDE on the dual $\mathfrak{g}^∗$ of the Lie algebra related to the Lie group manifold where the system is located, which is also the reduced form of a stochastic Hamiltonian system on the cotangent bundle of the Lie group by momentum mapping. Stochastic Poisson integrators are obtained by discretely reducing stochastic symplectic methods on the cotangent bundle to integrators on $\mathfrak{g}^∗.$ Stochastic generating functions creating stochastic symplectic methods are used to construct the schemes. An application to the stochastic rigid body system illustrates the theory and provides numerical validation of the method.
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来源期刊
CiteScore
2.10
自引率
9.10%
发文量
1
审稿时长
6-12 weeks
期刊介绍: The journal is directed to the broad spectrum of researchers in numerical methods throughout science and engineering, and publishes high quality original papers in all fields of numerical analysis and mathematical modeling including: numerical differential equations, scientific computing, linear algebra, control, optimization, and related areas of engineering and scientific applications. The journal welcomes the contribution of original developments of numerical methods, mathematical analysis leading to better understanding of the existing algorithms, and applications of numerical techniques to real engineering and scientific problems. Rigorous studies of the convergence of algorithms, their accuracy and stability, and their computational complexity are appropriate for this journal. Papers addressing new numerical algorithms and techniques, demonstrating the potential of some novel ideas, describing experiments involving new models and simulations for practical problems are also suitable topics for the journal. The journal welcomes survey articles which summarize state of art knowledge and present open problems of particular numerical techniques and mathematical models.
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