Response correction techniques for surrogate-based design optimization of microwave structures

Abstract

Simulation-based optimization has become an important design tool in microwave engineering. However, using electromagnetic (EM) solvers in the design process is a challenging task, primarily due to a high-computational cost of an accurate EM simulation. In this article, we present a review of EM-based design optimization techniques exploiting response-corrected physically based low-fidelity models. The surrogate models created through such a correction can be used to yield a reasonable approximation of the optimal design of the computationally expensive structure under consideration (high-fidelity model). Several approaches using this idea are reviewed including output space mapping, manifold mapping, adaptive response correction, and shape-preserving response prediction. A common feature of these methods is that they are easy to implement and computationally efficient. Application examples are provided.

Fast simulation-driven antenna design using response-feature surrogates

“…For the sake of comparison, the antenna was also optimized using the two benchmark methods: (i) space mapping (SM) and (ii) the pattern search algorithm . The SM algorithm utilized here exploits the low‐fidelity model R c as the underlying coarse model, and two types of model correction, specifically, frequency scaling and additive response correction . The SM surrogate is reset at every iteration using the high‐fidelity model data from the most current design; it is subsequently re‐optimized using pattern search.…”

Simulation-Driven Antenna Modeling by Means of Response Features and Confined Domains of Reduced Dimensionality

“…The first one are physics-based models, which exploit the specific knowledge of the system under design, usually in the form of an underlying low-fidelity model. Among many physics-based surrogate-assisted frameworks, space mapping techniques [31], response correction algorithms [32] or adaptive response scaling [33], but also feature-based optimization [34], may be listed. Good generalization capability of the physics-based surrogates is a result of a typically high correlation between the low-and high-fidelity models.…”

Response-correction-based fault detection in small linear microstrip patch arrays using magnitude-only far-field pattern samples

“…Both on‐off faults and partial faults are considered. For the sake of computational efficiency, the surrogate‐based modeling/optimization paradigm is utilized, where repeated evaluations of an expensive, EM‐simulation model are replaced by evaluations of its fast yet sufficient replacement, referred to as a surrogate. A notable example of a surrogate‐based optimization method popular in microwave engineering is space mapping (SM) .…”