Full-Body vs. Head-Only Modeling: Full Wave Computational SAR and Adaptation of Corresponding ANN Models

Electromagnetic compatibility (EMC) analysis is often computationally expensive, with partial modeling and domain-specific approximations commonly employed to improve efficiency, although these simplifications can introduce accuracy trade-offs. To address these challenges, this work focuses on bioelectromagnetic compatibility (Bio-EMC) problems, particularly the Specific Absorption Rate (SAR) calculations, by evaluating SAR in human head tissues using Full-Body and Head-Only models with finite element method (FEM) solvers under plane wave (PW) and near field (NF) exposures at 13.56 MHz. More than 2,000 full wave simulations are conducted, incorporating uncertainties in material properties and exposure angles, with machine learning techniques applied for enhanced analysis. Results show that while model truncation can impact SAR, certain scenarios allow Head-Only data to effectively replace Full-Body data. In these cases, parameter prioritization in artificial neural networks (ANNs) achieves over 90% accuracy while reducing input parameters by up to 70%. For cases where truncation effects are more significant, the ANN trained on Head-Only data is refined using Full-Body data, improving predictive accuracy up to 85% while maintaining computational efficiency. The proposed ANN-based approach enhances both computational efficiency and prediction reliability in Bio-EMC analysis, making it applicable to other emission susceptibility scenarios by reducing system complexity and improving the physical interpretation of results.