Computation of Laplacian eigenvalues of two-dimensional shapes with dihedral symmetry

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
David Berghaus, Robert Stephen Jones, Hartmut Monien, Danylo Radchenko
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引用次数: 0

Abstract

We numerically compute the lowest Laplacian eigenvalues of several two-dimensional shapes with dihedral symmetry at arbitrary precision arithmetic. Our approach is based on the method of particular solutions with domain decomposition. We are particularly interested in asymptotic expansions of the eigenvalues \(\lambda (n)\) of shapes with n edges that are of the form \(\lambda (n) \sim x\sum _{k=0}^{\infty } \frac{C_k(x)}{n^k}\) where x is the limiting eigenvalue for \(n\rightarrow \infty \). Expansions of this form have previously only been known for regular polygons with Dirichlet boundary conditions and (quite surprisingly) involve Riemann zeta values and single-valued multiple zeta values, which makes them interesting to study. We provide numerical evidence for closed-form expressions of higher order \(C_k(x)\) and give more examples of shapes for which such expansions are possible (including regular polygons with Neumann boundary condition, regular star polygons, and star shapes with sinusoidal boundary).

具有二面对称性的二维图形的拉普拉卡特征值计算
我们以任意精度算术数值计算了几种具有二面对称性的二维图形的最低拉普拉奇特征值。我们的方法基于域分解的特定解法。我们对具有 n 条边的形状的特征值 \(\lambda (n)\) 的渐近展开特别感兴趣,其形式为 \(\lambda (n) \sim x\sum _{k=0}^\{infty }.\其中 x 是 \(n\rightarrow\infty \)的极限特征值。以前只知道这种形式的展开适用于具有 Dirichlet 边界条件的正多边形,而且(令人惊讶的是)涉及黎曼zeta 值和单值多重zeta 值,这使它们成为有趣的研究对象。我们提供了高阶 \(C_k(x)\) 的闭式表达式的数值证据,并给出了更多可能有这种展开的形状的例子(包括具有诺伊曼边界条件的正多边形、正星形多边形和具有正弦边界的星形)。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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