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A fast direct matrix solver for surface integral equation methods for electromagnetic wave scattering from non-penetrable targets

机译:用于表面积分方程方法的快速直接矩阵求解器,用于从不可穿透目标散射电磁波

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The implementation details of a fast direct solver is described herein for solving dense matrix equations from the application of surface integral equation methods for electromagnetic field scatterings from non-penetrable targets. The proposed algorithm exploits the smoothness of the far field and computes a low rank decomposition of the off-diagonal coupling blocks of the matrices through a set of skeletonization processes. Moreover, an artificial surface (the Huygens’ surface) is introduced for each clustering group to efficiently account for the couplings between well-separated groups. Furthermore, a recursive multilevel version of the algorithm is presented. Although asymptotically the algorithm would not alter the bleak outlook of the complexity of the worst case scenario, O(N) for required CPU time where N denotes the number of unknowns, for electrically large electromagnetic (EM) problems; through numerical examples, we found that the proposed multilevel direct solver can scale as good as O(N) in memory consumption and O(N) in CPU time for moderate-sized EM problems. Note that our conclusions are drawn based on a few sample examples that we have conducted and should not be taken as a true complexity analysis for general electrodynamic applications. However, for the fixed frequency (h-refinement) scenario, where the discretization size decreases, the computational complexities observed agree well with the theoretical predictions. Namely, the algorithm exhibits O(N) and O(N) complexities for memory consumption and CPU time, respectively.
机译:本文描述了快速直接求解器的实现细节,用于通过应用表面积分方程方法求解不可穿透目标的电磁场散射来求解稠密矩阵方程。所提出的算法利用了远场的平滑度,并通过一组骨架化过程来计算矩阵的非对角耦合块的低秩分解。此外,为每个聚类组引入了一个人造表面(惠更斯表面),以有效地说明间隔良好的组之间的耦合。此外,提出了该算法的递归多级版本。尽管渐近算法不会改变最坏情况的复杂性,但对于电大电磁(EM)问题,所需的CPU时间为O(N),其中N表示未知数;通过数值示例,我们发现,对于中等规模的EM问题,建议的多级直接求解器的内存消耗可扩展至O(N),CPU时间可扩展至O(N)。请注意,我们的结论是基于我们进行的一些示例示例得出的,不应将其视为一般电动应用程序的真正复杂性分析。但是,对于离散频率减小的固定频率(h细化)方案,观察到的计算复杂度与理论预测非常吻合。即,该算法分别对于存储器消耗和CPU时间表现出O(N)和O(N)复杂度。

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