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A robust and reliable algorithm for computationally efficient design optimization of microwave structures evaluated with electromagnetic (EM) simulations is introduced. The presented approach utilizes a low-fidelity model constructed from coarse-discretization EM simulation of the structure under consideration, as well as cheap derivative information obtained by means of adjoint sensitivity. Adjoint sensitivity is exploited to reduce the misalignment between the low- and high-fidelity models through input space mapping, to construct a first-order consistent surrogate model through manifold mapping, and to optimize the surrogate through a trust-region-based algorithm. This comprehensive use of adjoint sensitivity results in a very low design cost and excellent convergence capability of the proposed algorithm. Efficiency of our approach is demonstrated using two examples: an ultra-wideband antenna and a microstrip bandpass filter. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:809–813, 2013; View this article online at wileyonlinelibrary.com. DOI: 10.1002/mop.27430

This work introduces a variable-fidelity optimization methodology for simulation-driven design optimization of filters. Our approach is based on electromagnetic (EM) simulations of different accuracy controlled by the mesh density. A Kriging interpolation model (the surrogate) is created using sampled low-fidelity EM data and optimized to approximately locate the optimum of the high-fidelity EM model of the filter. This initial surrogate is subsequently improved by blending in the high-fidelity data accumulated during the optimization process using the co-Kriging technique. The algorithm convergence is ensured by embedding it into the trust region framework. The operation and performance of our method is demonstrated using three filter design cases. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:765–769, 2013; View this article online at wileyonlinelibrary.com. DOI: 10.1002/mop.27447

SUMMARY A reliable methodology for accurate modeling of microwave filter is presented. Our approach exploits co-kriging that utilizes low-fidelity and high-fidelity electromagnetic simulation data and combines them into a single surrogate model. Densely sampled low-fidelity data determine a trend function, which is further corrected by sparsely sampled high-fidelity simulations. Low-fidelity electromagnetic data are also enhanced by using a frequency scaling to reduce its misalignment with the high-fidelity model. With our method, accurate models can be obtained at a fraction of the cost required by conventional approximation models that are exclusively based on high-fidelity simulations. Three examples of microstrip filters are considered for verification purposes. We also provide comparisons with conventional approximation models and include an application of co-kriging models for filter design optimization. Copyright © 2013 John Wiley & Sons, Ltd.

Antenna design is a multifaceted task that involves handling of various performance figures concerning both electrical performance of the structure as well as its geometry. Simultaneous control of several objectives through rigorous optimization is very challenging and virtually impossible through conventional approaches such as parameter sweeping. In this work, we investigate size reduction of ultra‐wideband antenna structures while taking into account the reflection response and total efficiency. Available design trade‐offs concerning the antenna footprint, its wideband matching, and efficiency are identified through numerical optimization. More specifically, the goal is to identify designs that exhibit the minimum possible footprint while maintaining the maximum in‐band reflection and average efficiency within the prescribed thresholds. This is realized by appropriate formulation of the objective function exploiting a penalty function approach. Our methodology is demonstrated using an ultra‐wideband monopole antenna example. As an extension, a 3‐objective Pareto set is further generated using surrogate modeling and multi‐objective evolutionary algorithm. A set of designs generated this way provides a designer with a comprehensive knowledge about the capabilities of a given antenna structure and facilitates a decision‐making process driven by a particular application and performance requirements