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arXiv 2201.01548math.NAcs.NA
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基于RBF插值的通量重建

On the Use of RBF Interpolation for Flux Reconstruction

Rob Watson, Will Trojak

摘要:

通量重建提供了一个框架,用于解决偏微分方程中的功能是不连续的元素内近似。通常,这通过使用多项式来完成。在这里,使用径向基函数作为一种方法,基本的功能近似探讨在一维,使用分析和数值方法。在一定的网格密度下,径向基函数通量重建优于多项式通量重建,并且随着径向基函数网格宽度的增加,网格密度的范围变得更细。一种方法,避免了不良的条件下,平坦的径向基函数被用来测试范围广泛的基础形状,并在非常小的值,多项式的行为被恢复。改变的解决方案点的位置被发现有一个效果类似于多项式FR,与高斯-勒让德点是最有效的。改变职能中心的位置被认为对业绩的影响非常小。对于非线性伯格斯方程也确定了类似的行为。

英文摘要:

Flux reconstruction provides a framework for solving partial differential equations in which functions are discontinuously approximated within elements. Typically, this is done by using polynomials. Here, the use of radial basis functions as a methods for underlying functional approximation is explored in one dimension, using both analytical and numerical methods. At some mesh densities, RBF flux reconstruction is found to outperform polynomial flux reconstruction, and this range of mesh densities becomes finer as the width of the RBF interpolator is increased. A method which avoids the poor conditioning of flat RBFs is used to test a wide range of basis shapes, and at very small values, the polynomial behaviour is recovered. Changing the location of the solution points is found to have an effect similar to that in polynomial FR, with the Gauss--Legendre points being the most effective. Altering the location of the functional centres is found to have only a very small effect on performance. Similar behaviours are determined for the non-linear Burgers' equation.

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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