Generalized Scattering Matrix Framework for Modeling Implantable Antennas in Multilayered Spherical Media

This paper presents a unified and efficient framework for analyzing antennas embedded in spherically stratified media -- a model broadly applicable to implantable antennas in biomedical systems and radome-enclosed antennas in engineering applications. The proposed method decouples the modeling of the antenna and its surrounding medium by combining the antenna's free-space generalized scattering matrix (GSM) with a set of extended spherical scattering operators (SSOs) that rigorously capture the electromagnetic interactions with multilayered spherical environments. This decoupling enables rapid reevaluation under arbitrary material variations without re-simulating the antenna, offering substantial computational advantages over traditional dyadic Green's function (DGF)-based MoM approaches. The framework supports a wide range of spherical media, including radially inhomogeneous and uniaxially anisotropic layers. Extensive case studies demonstrate excellent agreement with full-wave and DGF-based solutions, confirming the method's accuracy, generality, and scalability. Code implementations are provided to facilitate adoption and future development.

Careful! Training Relevance is Real

There is a recent proliferation of research on the integration of machine learning and optimization. One expansive area within this research stream is predictive-model embedded optimization, which uses pre-trained predictive models for the objective function of an optimization problem, so that features of the predictive models become decision variables in the optimization problem. Despite a recent surge in publications in this area, one aspect of this decision-making pipeline that has been largely overlooked is training relevance, i.e., ensuring that solutions to the optimization problem should be similar to the data used to train the predictive models. In this paper, we propose constraints designed to enforce training relevance, and show through a collection of experimental results that adding the suggested constraints significantly improves the quality of solutions obtained.