论文标题
大型域的强大DPG方法
A robust DPG method for large domains
论文作者
论文摘要
当解决大型域上的问题以及基于庞加莱型不平等的稳定性时，我们观察到DPG方法的鲁棒性缺乏鲁棒性。我们展示了如何使用适当缩放的测试规范来重新建立鲁棒性。作为模型案例，我们研究了泊松问题和Kirchhoff--Love板弯曲模型，还包括完全离散的变体，其中近似最佳的测试功能。这两个模型问题（包括An-异型域和混合边界条件）的数值实验证实了我们的发现。
We observe a dramatic lack of robustness of the DPG method when solving problems on large domains and where stability is based on a Poincaré-type inequality. We show how robustness can be re-established by using appropriately scaled test norms. As model cases we study the Poisson problem and the Kirchhoff--Love plate bending model, and also include fully discrete variants where optimal test functions are approximated. Numerical experiments for both model problems, including an-isotropic domains and mixed boundary conditions, confirm our findings.
