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

TL;DR

This paper introduces a physics-aware attention-based machine learning model that accurately and efficiently models mutual coupling in antenna arrays, significantly speeding up the process compared to traditional simulation methods.

Contribution

It presents a novel PC-LSTM neural network that incorporates physics principles and attention mechanisms for mutual impedance matrix extraction, improving interpretability and speed.

Findings

Achieves up to 7x faster impedance extraction than CST Microwave Studio.

Provides accurate mutual coupling modeling validated on five benchmarks.

Enhances physical interpretability through a physics-aware neural network design.

Abstract

This article presents a physics-aware convolutional long short-term memory (PC-LSTM) network for efficient and accurate extraction of mutual impedance matrices in dipole antenna arrays. By reinterpreting the Green's function through a physics-aware neural network 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 the Green's function matrix. These fused representations are then processed by a convolutional long short-term memory network, 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 7x speedup compared to CST Microwave Studio, making it a fast alternative to full-wave simulations for mutual coupling characterization.