A reliable model order reduction process for parametric analysis in electromagnetics is detailed. Special emphasis is placed on certifying the accuracy of the reduced-order model. For this purpose, a sharp state error estimator is proposed. Standard a posteriori state error estimation for model order reduction relies on the inf-sup constant. For parametric systems, the inf-sup constant is parameter-dependent. The a posteriori error estimation for systems with very small or vanishing inf-sup constant poses a challenge, since it is inversely proportional to the inf-sup constant, resulting in rather useless, overly pessimistic error estimators. Such systems appear in electromagnetics since the inf-sup constant values are close to zero at points close to resonant frequencies, where they eventually vanish. We propose a novel a posteriori state error estimator which avoids the calculation of the inf-sup constant. The proposed state error estimator is compared with the standard error estimator and a recently proposed one in the literature. It is shown that our proposed error estimator outperforms both existing estimators. Numerical experiments are performed on real-life microwave devices such as narrowband and wideband antennas, two types of dielectric resonator filters as well as a dual-mode waveguide filter. These examples show the capabilities and efficiency of the proposed methodology.
翻译:本文详细介绍了电磁学参数分析的可靠降阶模型过程,特别强调了保证减小阶数模型准确性的重要性。为此,我们提出了一种尖锐的状态误差估计方法。标准的后验状态误差估计依赖于等高常数,而对于参数系统,等高常数是参数依赖型的。对于等高常数非常小或为零的参数系统,后验误差估计会面临挑战,因为它与等高常数成反比例关系,导致误差估计器过于悲观和没意义。电磁学领域中常常会出现这种系统,因为等高常数值在接近共振频率的点上很接近于零,甚至可能消失。因此,我们提出了一种新型的后验状态误差估计器,避免了等高常数的计算。我们的提议被与标准误差估计器和最近文献中提出的误差估计器进行了比较。结果表明,我们提出的误差估计器表现优于现有的估计器。我们在狭带和宽带天线、两种介质谐振器过滤器以及双模波导滤波器等多种实际麻省理工学院和其他专业的微波器件上进行了数值实验,展示了本文提出方法的能力和效率。