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On a residual-based a posteriori error estimator for the total error

机译:在总基于剩余的后验误差估计器中的总误差

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A posteriori error analysis in numerical partial differential equations aims at providing sufficiently accurate information about the distance of the numerically computed approximation to the true solution. Besides estimating the total error, a posteriori analysis should also provide information about its discretization and (inexact) algebraic computation parts. This issue has been addressed by many authors using different approaches. Historically, probably the first and practically very important approach is based on combination of the classical residual-based bound on the discretization error with the adaptive hierarchy of discretizations and computations that allow to incorporate, using various heuristic arguments, the algebraic error. Motivated by some recent publications, this text uses a complementary approach and examines subtleties of the (generalized) residual-based a posteriori error estimator for the total error that rigorously accounts for the algebraic part of the error. The aim is to show on the standard Poisson model problem example, which is used here as a case study, that a rigorous incorporation of the algebraic error represents an intriguing problem that is not yet completely resolved. That should be of concern in h-adaptivity approaches, where the refinement of the mesh is determined using the residual-based a posteriori error estimator assuming Galerkin orthogonality. The commonly used terminology such as ‘guaranteed computable upper bounds’ should be in the presence of algebraic error cautiously examined.
机译:数字偏微分方程中的后验误差分析旨在提供关于对真实解决方案的数值计算近似的距离的足够准确的信息。除了估计总误差之外,后验分析还应提供有关其离散化和(不精确)代数计算部件的信息。许多作者使用不同方法的作者已经解决了这个问题。从历史上看,可能是第一个和实际非常重要的方法是基于分散化误差的古典残差绑定的组合,其中包括允许使用各种启发式参数,代数错误来结合的离散化错误和计算。通过一些最近的出版物,该文本使用互补方法,并检查(广义)基于残差的后验误差估计器的微妙误差估算器,用于严格地占错误的代数部分的总误差。该目的是在标准泊松模型问题示例上展示,这里用于作为一个案例研究,即代数误差的严格融合代表尚未完全解决的有趣问题。这应该是H-Adaptivity方法的关注,其中网格的改进使用假设Galerkin正交性的基于后验误差估计器来确定。常用的术语如“保证可计算的上限”应该在谨慎检查代数误差的情况下。

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