When Coarse EM Models Do Not Transfer: A Cross-Fidelity Study of Microwave Filter Optimization

Abstract

Lower-fidelity electromagnetic (EM) models are widely used to reduce the computational cost of microwave filter optimization, but their practical usefulness is often assessed through response similarity, own-fidelity objective values, or nominal simulation speedup. This paper examines whether designs optimized using coarse, mesh-derived EM models remain useful when verified at higher-fidelity. A cross-fidelity study is conducted using two microstrip filter benchmarks, a low-pass filter and a band-pass filter, under a fixed simulation setup, frequency sampling, objective function, and local-search procedure, so that mesh discretization is isolated as the source of model variation. Designs independently optimized at multiple mesh levels are reevaluated across the full model hierarchy, and their degradation during fine-model verification is used as an indicator of transferability. The results show that lower-fidelity success is not necessarily preserved under finer EM evaluation. In the band-pass filter case, extra coarse and coarse models satisfy the sampled transmission mask at their own-fidelity levels but yield substantially degraded responses in the fine model, whereas an intermediate model provides a more useful starting point for refinement. Space-mapping experiments further show that coarse-to-fine response matching does not always improve the target model objective value; its benefit depends on the selected model pair and on the observed transferability of the participating models. For the examples studied, the low-pass filter could be completed using an intermediate effective fine model, whereas the band-pass filter required an intermediate-to-fine progression followed by limited fine-model refinement. These findings highlight the need to evaluate coarse EM models by the survivability of their optimized designs, not only by speed or response similarity, before using them in multifidelity microwave filter optimization.