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SINC METHODS FOR DOMAIN DECOMPOSITION

机译:用于域分解的SINC方法

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Sine basis functions fond a desirable basis for solving singular problems via domain decomposition. This is because both the Sinc-Galerkin and sinc-collocation methods converge exponentially, even in the presence of boundary singularities. This suggests the future possibility of combining sine methods used in the vicinity of singularities with other methods, such as finite difference methods, used in the remainder of the original domain. For these reasons, a thorough investigation of the implementation of sine methods in the context of domain decomposition is the necessary first step. This work deals with sine methods for second-order ordinary differential equations with homogeneous Dirichlet boundary conditions. Both sinc-collocation and Sinc-Galerkin methods are presented. The two traditional methods of domain decomposition, overlapping and patching, are described. Thus all the groundwork is laid to readily determine which method is most suited to any given problem. Numerical results are presented for both decomposition methods that exhibit the nearly identical errors achieved whether one uses the sinc-collocation method or the Sino-Galerkin method. [References: 19]
机译:正弦基函数为通过域分解解决奇异问题提供了理想的基础。这是因为,即使存在边界奇异点,Sinc-Galerkin方法和Sinc-colocation方法都呈指数收敛。这表明将来可能会将奇异点附近使用的正弦方法与原始域其余部分中使用的其他方法(例如有限差分方法)结合起来。由于这些原因,在域分解的背景下彻底研究正弦方法的实现是必要的第一步。这项工作涉及具有齐次Dirichlet边界条件的二阶常微分方程的正弦方法。介绍了Sinc-colocation和Sinc-Galerkin方法。描述了域分解的两种传统方法,即重叠和修补。因此,所有基础工作都可以轻松确定哪种方法最适合任何给定的问题。两种分解方法的数值结果均显示出几乎相同的误差,无论是使用正弦搭配方法还是使用Sino-Galerkin方法。 [参考:19]

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