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arXiv 2312.09752math.NAcs.NA
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一种面向空间网络模型的代数多尺度方法

An algebraic multiscale method for spatial network models

Moritz Hauck, Roland Maier, Axel Målqvist

AI总结:

本研究提出一种基于局部正交分解(LOD)的全代数多尺度方法,可对空间网络模型进行可靠粗尺度近似,适用于复杂几何对象与椭圆偏微分方程离散,且经严格误差分析和数值算例验证。

摘要:

在本研究中,我们提出了一种多尺度方法,用于对由图节点相关的线性方程组表示的空间网络模型进行可靠的粗尺度近似。该方法基于局部正交分解(Localized Orthogonal Decomposition,LOD)策略的思想,以完全代数的方式构建,可应用于瓦楞纸板等几何结构复杂的对象。特别地,该方法还可应用于椭圆偏微分方程的有限差分或有限元离散,得到的近似解具有与连续情形下LOD相似的性质。我们在对网络的合理假设下,对所提方法进行了严格的误差分析,此外还给出了数值算例,验证了我们的理论结果。

英文摘要:

In this work, we present a multiscale approach for the reliable coarse-scale approximation of spatial network models represented by a linear system of equations with respect to the nodes of a graph. The method is based on the ideas of the Localized Orthogonal Decomposition (LOD) strategy and is constructed in a fully algebraic way. This allows to apply the method to geometrically challenging objects such as corrugated cardboard. In particular, the method can also be applied to finite difference or finite element discretizations of elliptic partial differential equations, yielding an approximation with similar properties as the LOD in the continuous setting. We present a rigorous error analysis of the proposed method under suitable assumptions on the network. Moreover, numerical examples are presented that underline our theoretical results.

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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