On the Coefficients in Finite Difference Series Expansions of Derivatives

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
J. W. Banks, W. D. Henshaw
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 63, Issue 5, Page 2009-2025, October 2025.
Abstract. The formulation of finite difference approximations is a classical problem in numerical analysis. In this article, we consider difference approximations that are based on a series expansion in powers of the second undivided difference. Each additional term in the series increases the order of accuracy by two. These expansions are useful in a variety of contexts such as in the development of modified equation schemes, the design of high-order accurate energy stable discretizations, and error analysis of certain finite element or finite difference schemes. Here, we provide closed form expressions for the coefficients in the series expansions for derivatives of all orders. We also provide some short recursions defining the series coefficients, and formulae for the stencil coefficients in standard difference approximations. The series expansions are used to show some useful properties of the Fourier symbols of difference approximations and to derive rules of thumb for the number of points-per-wavelength needed to achieve a given error tolerance when solving wave propagation problems involving higher spatial derivatives.
导数有限差分级数展开式中的系数
SIAM数值分析杂志,第63卷,第5期,2009-2025页,2025年10月。摘要。有限差分近似的表达式是数值分析中的一个经典问题。在本文中,我们考虑基于二阶不可除差的幂级数展开的差分近似。序列中每增加一项,精度的阶数就增加两。这些展开式在各种情况下都很有用,如改进方程格式的开发,高阶精确能量稳定离散化的设计,以及某些有限元或有限差分格式的误差分析。这里,我们提供了所有阶导数级数展开式中系数的封闭形式表达式。我们还提供了一些定义级数系数的短递推式,以及标准差分近似中模板系数的表达式。级数展开式用于显示差分近似的傅里叶符号的一些有用的性质,并推导出在解决涉及更高空间导数的波传播问题时实现给定容错所需的每个波长的点数的经验法则。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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