标题： 基于物理感知注意力机制的机器学习新方法用于互耦建模 显示英文标题

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

摘要： 本文提出了一种物理感知的卷积长短期记忆网络（PC-LSTM），用于在偶极子天线阵列中高效且准确地提取互阻抗矩阵。通过通过物理感知神经网络重新解释格林函数，并将其嵌入自适应损失函数中，所提出的基于机器学习的方法在互耦建模中实现了增强的物理可解释性。此外，精心设计了一个注意力机制，通过融合格林函数矩阵的实部和虚部来校准复数值特征。这些融合表示随后由卷积长短期记忆网络处理，最终得出线性天线阵列的阻抗矩阵。与五个基准进行验证表明了所提方法的有效性，与CST Microwave Studio相比，展示了准确的阻抗提取，速度提高了多达7倍，使其成为互耦特性全波仿真的一种快速替代方案。 显示英文摘要

摘要： 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.