PC-LSTM predicts port impedance matrices of dipole arrays with attention-based feature fusion and a hand-crafted physics kernel, but errors vs CST and a weak LSTM-to-MoM derivation undermine the accuracy claims.
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Novel Physics-Aware Attention-Based Machine Learning Approach for Mutual Coupling Modeling
PC-LSTM predicts port impedance matrices of dipole arrays with attention-based feature fusion and a hand-crafted physics kernel, but errors vs CST and a weak LSTM-to-MoM derivation undermine the accuracy claims.