Frank Pedersen

A space-mapping interpolating surrogate algorithm for highly optimized EM-based design of microwave devices

We justify and elaborate in detail on a powerful new optimization algorithm that combines space mapping (SM) with a novel output SM. In a handful of fine-model evaluations, it delivers for the first time the accuracy expected from classical direct optimization using sequential linear programming. Our new method employs a space-mapping-based interpolating surrogate (SMIS) framework that aims at locally matching the surrogate with the fine model. Accuracy and convergence properties are demonstrated using a seven-section capacitively loaded impedance transformer. In comparing our algorithm with major minimax optimization algorithms, the SMIS algorithm yields the same minimax solution within an error of 10/sup -15/ as the Hald-Madsen algorithm. A highly optimized six-section H-plane waveguide filter design emerges after only four HFSS electromagnetic simulations, excluding necessary Jacobian estimations, using our algorithm with sparse frequency sweeps.

Space mapping interpolating surrogates for highly optimized EM-based design of microwave devices

We present a powerful optimization algorithm that incorporates space mapping (SM) and the new output space mapping (OSM) to yield highly optimized results in a handful of fine model evaluations. Our new method employs an SM-based interpolating surrogate (SMIS) framework that aims at matching the surrogate with the fine model locally. Accuracy and convergence properties are demonstrated using a seven-section capacitively-loaded impedance transformer. A highly optimized six-section H-plane waveguide filter design emerges after only four HFSS EM simulations, excluding necessary Jacobian estimations, using the new algorithm with sparse frequency sweeps.