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arXiv 1707.03201math.NAcs.NA
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基于函数型的自适应IgA方案中误差控制方法

Functional approach to the error control in adaptive IgA schemes for elliptic boundary value problems

RICAM Linz, Johann Radon Institute(林茨RICAM,约翰·拉德恩研究所)

Svetlana Matculevich

AI总结:

本文研究了椭圆边值问题中自适应IgA方案的函数型后验误差估计,提出了一种可靠的解近似算法和高效的后验误差界生成方案,利用B-(THB-)样条的高光滑性实现更粗的网格和更少的未知数。

摘要:

本文介绍了在椭圆边值问题中针对IgA近似方案的函数型后验误差估计的数值研究。我们详细讨论了此类估计的关键性质,并提出了一个可靠的解近似算法及基于求解辅助问题的高效后验误差界生成方案。该方法利用B-(THB-)样条的高光滑性来重构辅助向量函数,从而允许使用更粗的网格并显著减少未知数数量。在论文第二部分中,展示了系统测试误差估计所获得的最具有代表性的数值结果。从误差估计(指示)和计算成本的角度分析了所获得误差界的有效性。几个例子表明,函数型误差估计(也称为偏差与精确解的上界和下界)比残差基误差估计具有更精确的误差控制。同时,生成误差上界所需的辅助变量重构的组装和求解过程比主未知数的进程快得多。

英文摘要:

This work presents a numerical study of functional type a posteriori error estimates for IgA approximation schemes in the context of elliptic boundary-value problems. Along with the detailed discussion of the most crucial properties of such estimates, we present the algorithm of a reliable solution approximation together with the scheme of efficient a posteriori error bound generation-based on solving an auxiliary problem with respect to an introduced vector-valued variable. In this approach, we take advantage of B-(THB-)spline's high smoothness for the auxiliary vector function reconstruction, which, at the same time, allows to use much coarser meshes and decrease the number of unknowns substantially. The most representative numerical results, obtained during a systematic testing of error estimates, are presented in the second part of the paper. The efficiency of the obtained error bounds is analysed from both the error estimation (indication) and the computational expenses points of view. Several examples illustrate that functional error estimates (alternatively referred to as the majorants and minorants of deviation from an exact solution) perform a much sharper error control than, for instance, residual-based error estimates. Simultaneously, assembling and solving the routines for an auxiliary variable reconstruction which generate the majorant of an error can be executed several times faster than the routines for a primal unknown.

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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