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arXiv 2007.08824math.NAcs.NA
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对偶间断Galerkin范数下协调残差极小化方法面向目标的自适应性

Goal-oriented adaptivity for a conforming residual minimization method in a dual discontinuous Galerkin norm

Sergio Rojas, David Pardo, Pouria Behnoudfar, Victor M. Calo

摘要:

本文提出了一种面向目标的网格自适应算法,用于求解对偶间断Galerkin范数下的残差最小化稳定的有限元法。通过解决鞍点问题,这种残差最小化提供了对每个网格实例上的解的稳定连续近似和到破碎多项式空间上的残差投影,这是一种鲁棒的误差估计器,通过自动网格细化来最小化离散能量范数。在这项工作中,我们提出并分析了一个面向目标的自适应算法,这种稳定的残差最小化。我们解决了原始和伴随问题,考虑相同的鞍点制定和不同的右手边。通过解决第三个稳定的问题,我们得到了两个有效的误差估计,以指导面向目标的自适应。我们说明了这种目标导向的自适应策略对流扩散反应问题的性能。

英文摘要:

We propose a goal-oriented mesh-adaptive algorithm for a finite element method stabilized via residual minimization on dual discontinuous-Galerkin norms. By solving a saddle-point problem, this residual minimization delivers a stable continuous approximation to the solution on each mesh instance and a residual projection onto a broken polynomial space, which is a robust error estimator to minimize the discrete energy norm via automatic mesh refinement. In this work, we propose and analyze a goal-oriented adaptive algorithm for this stable residual minimization. We solve the primal and adjoint problems considering the same saddle-point formulation and different right-hand sides. By solving a third stable problem, we obtain two efficient error estimates to guide goal-oriented adaptivity. We illustrate the performance of this goal-oriented adaptive strategy on advection-diffusion-reaction problems.

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