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Fast and accurate replacement models (surrogates) are indispensable in the design of microwave and antenna structures. Conventional data-driven modeling of antennas is usually prohibitively expensive in terms of data acquisition due to highly nonlinear responses of typical responses (e.g., reflection coefficient versus frequency). In this paper, a technique for reduced-cost modeling is proposed. The...
Reliable yet fast surrogate models are indispensable in the design of contemporary antenna structures. Data-driven models, e.g., based on Gaussian Processes or support-vector regression, offer sufficient flexibility and speed, however, their setup cost is large and grows very quickly with the dimensionality of the design space. In this paper, we propose cost-efficient modeling of antenna structures...
In this work, we propose a computationally efficient procedure for dimension scaling of antenna structures. The fundamental component of our methodology is an inverse surrogate model that relates geometry parameters of the antenna structure of interest to its operating frequency. The surrogate is constructed using several antenna designs obtained at the level of the coarse-discretization EM simulation...
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