We observe a dramatic lack of robustness of the DPG method when solving problems on large domains and where stability is based on a Poincar\'e-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.
翻译:我们观察到,在解决大域的问题时,以及在稳定以Poincar\'e型不平等为基础的情况下,DPG方法严重缺乏稳健性。我们展示了如何通过使用适当规模的测试规范来重新建立稳健性。作为示范案例,我们研究了Poisson问题和Kirchhoff-love板块弯曲模式,并包括完全分离的变量,这些变量的测试功能大致相同。对两个模型问题的数值实验,包括反异域和混合边界条件,证实了我们的结论。