There is a wide number of problems in electromagnetic (EM) engineering that require a real-time response or in which the input–output relationship is not a-priori known or cannot be defined due to ...

Learning-by-examples techniques as applied to electromagnetics

A robust algorithm for simulation-driven design optimization of microwave structures evaluated using CST MICROWAVE STUDIO® is described. The algorithm exploits gradient information obtained using adjoint sensitivity (if available) or finite differentiation. It also uses trust region approach that ensures good convergence properties and improves the overall performance. The efficiency of our approach is demonstrated using several examples of microwave structures. We also discuss an extension of the algorithm where the sensitivity of complex-valued responses rather than of the real-valued ones is used. A performance comparison with other optimization techniques is also provided. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.

Robust microwave design optimization using adjoint sensitivity and trust regions

A two-stage approach based on Gaussian process regression that achieves significantly reduced requirements for computationally expensive high-fidelity training data is presented for the modeling of planar antenna input characteristics Our method involves variable-fidelity electromagnetic simulations In the first stage, a mapping between electromagnetic models (simulations) of low and high fidelity is learned, which allows us to substantially reduce (by 80% or more) the computational effort necessary to set up the high-fidelity training data sets for the actual surrogate models (second stage), with negligible loss in predictive power We illustrate our method by modeling the input characteristics of three antenna structures with up to seven design variables The accuracy of the two-stage method is confirmed by the successful use of the surrogates within a space-mapping-based optimization/design framework

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Two-Stage Framework for Efficient Gaussian Process Modeling of Antenna Input Characteristics

Full-wave electromagnetic analysis has become one of the major design tools for contemporary antenna structures. Although reliable, it is computationally expensive, which makes automated simulation-driven antenna design (e.g., parametric optimization) difficult. This difficulty can be alleviated by utilization of fast and accurate replacement models (surrogates). Unfortunately, conventional data-driven modeling of antennas is usually prohibitively expensive in terms of data acquisition or even infeasible (due to numerical problems with handling large training datasets). In this letter, a technique for reduced-cost antenna modeling is discussed. It exploits constrained sampling within a restricted region of the design space, defined by the reference designs optimized for selected sets of performance requirements. The proposed approach permits dramatic reduction of the sampled portion of the design space without formally reducing geometry parameter ranges. It is demonstrated using two antenna structures and favorably compared to conventional modeling using kriging interpolation.

Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling

This paper presents an automated procedure to determine the electric or magnetic near-field profile of electronic systems and devices in a given plane. It combines sequential sampling to determine the optimal coordinates of near-field scan points at arbitrary coordinates in the scanning plane. The effectiveness of the approach is illustrated by applying it to both a simulated and a measured printed circuit board example.

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Automated Near-Field Scanning Algorithm for the EMC Analysis of Electronic Devices

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T. Dhaene

Papers

Surrogate-based infill optimization applied to electromagnetic problems: Surrogate-Based Optimization of EM Systems