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

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 (macrolevel) size and aperiodic (microscale) layouts. Such an approach leverages 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 nonuniform coupling effects arising when the UCs are irregularly assembled to build the corresponding SPSS. Toward 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 illustrate the radiation features of the resulting layouts, validated with accurate full-wave simulations, as well.

Automated summary

In this study, an innovative design process for static passive smart skins is proposed, taking into consideration electromagnetic interactions due to their finite size and aperiodic layouts. The process combines an inverse source formulation to define surface currents and a system-by-design paradigm to synthesize unit cell descriptors. An enhanced artificial intelligence-based digital twin is built to predict relationships among unit cells and nonuniform coupling effects, creating a training database for implementation.