Validation-Aware Surrogate Shortlisting for Biomedical Microwave Imaging : Analytical Performance and FDTD Transfer

Abstract

Broadband antenna, frequency and channel selection requires efficient prioritisation of finite candidate configurations, yet strong surrogate performance within a simplified analytical model does not ensure that the learned ordering will transfer to another electromagnetic representation. This study developed a validation-aware surrogate-shortlisting framework using a controlled breast-mimetic benchmark comprising 120 scenarios and 80 antenna–frequency–channel candidates per scenario. Surrogate models were developed using grouped scenario-level validation, and the model and five-candidate shortlist policy were frozen before external evaluation. Random forest produced the lowest-regret analytical-domain shortlist and remained stable across model-initialisation seeds. The frozen ranking was then challenged on held-out scenarios using a separately implemented restricted two-dimensional transverse-magnetic finite-difference time-domain model. Although numerical-reference checks supported shortlist-level use of the operational grid, candidate ordering did not transfer reliably: optimum inclusion, shortlist agreement and rank association were weak, although a qualified candidate within 2 mm of the finite-library FDTD optimum was retained in 58.3% of cases. Transfer failure varied by frequency band and channel family, while incomplete alignment between the analytical and FDTD candidate libraries prevented attribution of the discrepancy to electromagnetic-model shift alone. The framework therefore positions analytical surrogates as auditable shortlisting tools that reduce downstream candidate-level assessment while retaining independent electromagnetic evaluation before final design selection.