An electromagnetic system can be described in a variety of ways. Coarse models provide fast evaluations but lack the required accuracy in the final stages of design. Fine models are highly accurate, but prohibitively expensive. Finding a compromise between these extremes may assist in overcoming bottlenecks in design automation and optimization. One approach is to carry out optimization in the coarse model space and use fine model simulations to fine-tune the result via space mapping. A new response surface space mapping (RSSM) strategy is presented and applied to an E-shaped patch antenna test case. The solutions that emerge are comparable to full fine model optimization at a fraction of the cost.Index Terms-Electromagnetic modeling, electromagnetic optimization, response surface (RS) models, space mapping (SM).

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Finite-element (FE) accuracy can be directly affected by mesh quality. The potential benefits and related costs of a family of new mesh quality improvement systems are investigated using a suite of electromagnetic benchmarks and mesh quality measures theoretically linked to FE accuracy. Experimental findings suggest error reductions on the order of 10% for half-second runtimes.Index Terms-Adaptive systems, electromagnetic analysis, finite-element methods (FEMs), mesh improvement, mesh quality.

Developing models from computational data is a major focus in electromagnetic design. This paper introduces ways of creating customized neural models based on a fuzzy clustering of responses. Fuzzy-clustered neural network (FCNN) models are explored, leading to increases in accuracy. The information contained within FCNN models can also be applied to space mapping electromagnetic optimization. This optimization approach strives to combine the accuracy of fine models (such as finite elements) with the low cost of coarse models. These FCNN enhancements are demonstrated through a patch antenna test case.