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Goal-oriented error estimation and adaptivity for free-boundary problems: The domain-map linearization approach

机译:自由边界问题的面向目标的误差估计和适应性:域图线性化方法

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In free-boundary problems, the accuracy of a goal quantity of interest depends on both the accuracy of the approximate solution and the accuracy of the domain approximation. We develop duality-based a posteriori error estimates for functional outputs of solutions of free-boundary problems that include both sources of error. The derivation of an appropriate dual problem (linearized adjoint) is, however, nonobvious for free-boundary problems. To derive an appropriate dual problem, we present the domain-map linearization approach. In this approach, the free-boundary problem is first transformed into an equivalent problem on a fixed reference domain after which the dual problem is obtained by linearization with respect to the domain map. We show for a Bernoulli-type free-boundary problem that this dual problem corresponds to a Poisson problem with a nonlocal Robin-type boundary condition. Furthermore, we present numerical experiments that demonstrate the effectivity of the dual-based error estimate and its usefulness in goal-oriented adaptive mesh refinement.
机译:在自由边界问题中,目标目标量的精度取决于近似解的精度和域近似的精度。我们为包括两个错误源的自由边界问题的解决方案的功能输出开发基于对偶的后验误差估计。但是,对于自由边界问题,推导适当的对偶问题(线性伴随)并不明显。为了得出适当的对偶问题,我们提出了域图线性化方法。在这种方法中,首先将自由边界问题转换为固定参考域上的等效问题,然后通过相对于域图进行线性化获得对偶问题。对于伯努利型自由边界问题,我们证明了该对偶问题与具有非局部罗宾型边界条件的泊松问题相对应。此外,我们目前的数值实验证明了基于对偶的误差估计的有效性及其在面向目标的自适应网格细化中的有用性。

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