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A-posteriori compression of wavelet-BEM matrices

机译:小波-BEM矩阵的后验压缩

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The success of the wavelet boundary element method (BEM) depends on its matrix compression capability. The wavelet Galerkin BEM (WGBEM) based on non-standard form (NS-form) in Tausch (J Numer Math 12(3): 233–254, 2004) has almost linear memory and time complexity. Recently, wavelets with the quasi-vanishing moments (QVMs) have been used to decrease the constant factors involved in the complexity estimates (Xiao in Comput Methods Appl Mech Eng 197:4000–4006, 2008). However, the representations of layer potentials in QVM bases still have much more negligible entries than predicted by a-priori estimates, which are based on the separation of the supports of the source- and test-wavelets. In this paper, we introduce an a-posteriori compression strategy, which is designed to preserve the convergence properties of the underlying Galerkin discretization scheme. We summarize the different compression schemes for the WGBEM and demonstrate their performances on practical problems including Stokes flow, acoustic scattering and capacitance extraction. Numerical results show that memory allocation and CPU time can be reduced several times. Thus the storage for the NS-form is typically less than what is required to store the near-field interactions in the well-known fast multipole method.
机译:小波边界元方法(BEM)的成功取决于其矩阵压缩能力。基于Tausch中非标准格式(NS-form)的小波Galerkin BEM(WGBEM)(J Numer Math 12(3):233–254,2004)具有几乎线性的内存和时间复杂性。最近,具有准消失矩(QVM)的小波已被用于减少复杂性估计中涉及的常数因子(Xiao in Comput Methods Appl Mech Eng 197:4000–4006,2008)。但是,与基于先验估计的预测(基于源和测试小波的支持的分离)相比,QVM基础中层电势的表示仍然具有微不足道的条目。在本文中,我们介绍了一种后验压缩策略,该策略旨在保留底层Galerkin离散化方案的收敛性。我们总结了WGBEM的不同压缩方案,并展示了它们在实际问题上的性能,包括斯托克斯流,声散射和电容提取。数值结果表明,内存分配和CPU时间可以减少数倍。因此,NS形式的存储通常少于众所周知的快速多极方法中存储近场相互作用所需的存储。

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