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In this paper, a low-cost formulation of space mapping (SM) for design optimization of antenna structures is investigated. The major challenge is that low-fidelity (or coarse) antenna models are normally obtained from coarse-discretization EM simulations so that their evaluation cost cannot be neglected. Consequently, the SM algorithm has to aim at reducing both the number of fine and coarse model...
In this paper, we present a maximally flat quadratic interpolation (MFQI) enhanced input space mapping modeling technique. The linear input mapping is enhanced by an MFQI input correction term to compensate for the nonlinear mismatch between the fine model and its surrogate within the region of interest. We demonstrate the accuracy improvement using a microstrip bend example.
<?Pub Dtl?>We present a robust space mapping (SM) algorithm exploiting electromagnetic (EM)-based adjoint sensitivities for microwave design optimization. Our approach utilizes low-cost EM-based adjoint sensitivities and trust region methods to improve an SM algorithm at three levels, which are: 1) to build a better overall surrogate while ensuring convergence; 2) to speed up and safeguard the...
A simplified schematic is presented to implement fast and accurate space-mapping-based modeling and design. It can match a surrogate model with both fine model responses and approximated responses. The implementation allows us to study aspects of modeling, including sample selection, time cost and accuracy. A nominal design is obtained with selected models and verified through high-fidelity EM simulations...
A space mapping technique is presented that utilizes recent commercially available adjoint sensitivity analysis procedures for modeling microwave and RF components. It is shown that using only one full-wave electromagnetic simulation along with an adjoint sensitivity evaluation one can build a good surrogate model. The proposed technique calibrates a surrogate by matching responses and corresponding...
Coarse model is a critical component of the space mapping (SM) optimization algorithm. It should be fast and reasonably accurate. A popular choice of the coarse model is an equivalent circuit. Circuit models are computationally cheap but they are not always accurate, and in many cases (e.g., antennas) they are not available. Coarsely-discretized EM models are available for all structures, they are...
The performance of the space mapping (SM) optimization algorithm depends both on approximation and generalization capabilities of the underlying surrogate model. Often, the surrogate is selected by trial and error which may lead to excessive computational overhead and poor quality of the optimization outcome. Here, we introduce an adaptively constrained parameter extraction process to automatically...
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