Abstract-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 15 as the Hald-Madsen algorithm. A highly optimized six-section -plane waveguide filter design emerges after only four HFSS electromagnetic simulations, excluding necessary Jacobian estimations, using our algorithm with sparse frequency sweeps.Index Terms-Computer-aided design (CAD) algorithms, electromagnetics, filter design, interpolating surrogate, microwave modeling, optimization, output space mapping (OSM), space mapping (SM), surrogate modeling.
Abstract-We present a comprehensive microwave design framework for implementing the original, aggressive, implicit, and response residual space-mapping (SM) approaches through widely available software. General steps and tools for possible SM implementations are elaborated. Our presentation is a reference guide for microwave designers using the SM technique. An instructive "multiple cheese-cutting" example demonstrates the SM approach to engineering design and some possible pitfalls. For the first time, an ADS framework implements the SM steps interactively. A three-section transformer example illustrates the approach, step by step. A six-section -plane waveguide filter design emerges after four iterations, using the implicit SM and the response-residual space-mapping (RRSM) optimization entirely within the design framework. An RRSM surrogate is developed to match the fine (HFSS) model. We use sparse frequency sweeps and do not require Jacobians of the fine model. Index Terms-Computer-aided design (CAD), engineering optimization, filter design, parameter extraction (PE), space mapping (SM), surrogate modeling.
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