Building a Smart EM Environment -- AI-Enhanced Aperiodic Micro-Scale Design of Passive EM Skins

Abstract

An innovative process for the design of static passive smart skins (SPSSs) is proposed to take into account, within the synthesis, the electromagnetic (EM) interactions due to their finite (macro-level) size and aperiodic (micro-scale) layouts. Such an approach leverages on the combination of an inverse source (IS) formulation, to define the SPSS surface currents, and of an instance of the System-by-Design paradigm, to synthesize the unit cell (UC) descriptors suitable for supporting these currents. As for this latter step, an enhanced Artificial Intelligence (IA)-based digital twin (DT) is built to efficiently and reliably predict the relationships among the UCs and the non-uniform coupling effects arising when the UCs are irregularly assembled to build the corresponding SPSS. Towards this end and unlike state-of-the-art approaches, an aperiodic finite small-scale model of the SPSS is derived to generate the training database for the DT implementation. A set of representative numerical experiments, dealing with different radiation objectives and smart skin apertures, is reported to assess the reliability of the conceived design process and to illustrate the radiation features of the resulting layouts, validated with accurate full-wave simulations, as well.