Novel Physics-Aware Attention-Based Machine Learning Approach for Mutual Coupling Modeling

This article presents a physics-aware convolutional long short-term memory (PC-LSTM) network for efficient and accurate modeling of mutual impedance matrices in dipole antenna arrays. By reinterpreting Green’s function through a physics-aware neural network (PANN) and embedding it into an adaptive loss function, the proposed machine learning-based approach achieves enhanced physical interpretability in mutual coupling modeling. Also, an attention mechanism is carefully designed to calibrate complex-valued features by fusing the real and imaginary parts of Green’s function matrix. These fused representations are subsequently fed into a convolutional long short-term memory network incorporating a physics-aware convolution kernel, and the impedance matrix of the linear antenna array can be finally derived. Validation against five benchmarks underscores the efficacy of the proposed approach, demonstrating accurate impedance extraction with up to a $7\times $ speedup compared to CST Microwave Studio, making it a fast alternative to full-wave simulations for mutual coupling characterization.