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arXiv 1410.6293math.NAcs.NA
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随机Runge-Kutta方法对二次不变量的近似保持

Near Preservation of Quadratic Invariants by Stochastic Runge-Kutta Methods

School of Mathematical Sciences, University of Chinese Academy of Sciences(中国科学院大学数学科学学院)

Jialin Hong, Lijin Wang, Dongsheng Xu, Liying Zhang

AI总结:

本文研究了显式随机Runge-Kutta方法保持二次不变量的条件,提出构造近保守方法的实用途径,并通过数值实验验证方法在保持不变量方面的表现。

摘要:

基于根着色树的组合理论,我们研究了显式随机Runge-Kutta(SRK)方法保持二次不变量(QI)到一定精度阶的条件。这些条件可以提供一种构造显式近保守SRK方法的实用方法。同时,我们估计了隐式保守SRK方法在固定点迭代和牛顿迭代中保持QI误差的估计。最后,通过数值实验测试方法在保持QI方面的行为。

英文摘要:

Based on the combinatory theory of rooted colored trees, we investigate the conditions for the explicit stochastic Runge-Kutta (SRK) methods to preserve quadratic invariants (QI) up to certain orders of accuracy. These conditions can supply a practical approach of constructing explicit nearly conservative SRK methods. Meanwhile, we estimate errors in the preservation of QI resulting from iterative implementation of implicit conservative SRK methods with fixed-point and Newton's iterations. Finally, numerical experiments are performed to test the behavior of the methods in preserving QI.

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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