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arXiv 0802.2674math.NAcs.NA
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泊松方程无网格有限差分方法中的最小正模板

Minimal positive stencils in meshfree finite difference methods for the Poisson equation

Massachusetts Institute of Technology(麻省理工学院)

Benjamin Seibold

AI总结:

针对泊松方程的无网格有限差分方法,提出一种在点云上生成最小尺寸正模板的方法,并给出存在正模板的点云几何条件,与最小二乘法在精度和计算性能上进行比较。

摘要:

泊松方程的无网格有限差分方法在点云上近似拉普拉斯算子。理想的是正模板,即所有邻域条目符号相同。经典的最小二乘法产生通常非正的大模板。我们提出一种方法,生成最小尺寸的正模板。我们给出点云几何条件,使得正模板总是存在。新的离散化方法与最小二乘法在精度和计算性能上进行了比较。

英文摘要:

Meshfree finite difference methods for the Poisson equation approximate the Laplace operator on a point cloud. Desirable are positive stencils, i.e. all neighbor entries are of the same sign. Classical least squares approaches yield large stencils that are in general not positive. We present an approach that yields stencils of minimal size, which are positive. We provide conditions on the point cloud geometry, so that positive stencils always exist. The new discretization method is compared to least squares approaches in terms of accuracy and computational performance.

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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