Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human body localization and sensing. Physics-informed Generative Neural Network (GNN) models have been recently proposed to reproduce EM effects, namely to simulate or reconstruct missing data or samples by incorporating relevant EM principles and constraints. The paper discusses a Variational Auto-Encoder (VAE) model which is trained to reproduce the effects of human motions on the EM field and incorporate EM body diffraction principles. Proposed physics-informed generative neural network models are verified against both classical diffraction-based EM tools and full-wave EM body simulations.

翻译：用于预测射频传播的电磁人体模型耗时较长，这阻碍了其在严格实时计算成像问题（如人体定位与感知）中的应用。近期提出的物理知识生成神经网络模型通过融入相关电磁原理与约束，能够复现电磁效应，即模拟或重构缺失数据或样本。本文讨论了一种变分自编码器模型，该模型经过训练可再现人体运动对电磁场的影响，并融入了电磁人体绕射原理。所提出的物理知识生成神经网络模型已通过经典绕射电磁工具及全波电磁人体仿真进行了验证。