Deep Learning Enhanced Hybrid BEM-rMLNCA Method for Efficient Power Cable Crosstalk Prediction
DOI:
https://doi.org/10.13052/2026.ACES.J.410705Keywords:
Boundary element method, Deep Neural Network, far-end crosstalk, near-end crosstalk, power cable, recompressed multilevel nested cross approximationAbstract
This paper presents a deep learning-assisted hybrid Boundary Element Method (BEM)-recompressed Multilevel Nested Cross Approximation (rMLNCA) algorithm for efficient and accurate crosstalk prediction. This hybrid approach aims to combine the physics-based precision of BEM with the compression efficiency of rMLNCA and the nonlinear learning capability of deep neural networks (DNN) for high-fidelity modeling with reduced computational requirements. Under this proposed method, three parameters are predicted for crosstalk: Near-End Crosstalk (NEXT), Far-End Crosstalk (FEXT), and Common-Mode Current (CMC) measurement. The impedance and coupling parameters are computed using BEM, while rMLNCA decomposes and recompresses the far-field matrices into low-rank admissible blocks, reducing both memory and time significantly. It improves predictive accuracy further by learning nonlinear relationships among geometric, material, and frequency-dependent features using an optimized deep neural network architecture. The suggested model has high predictive consistency with the 5-fold cross-validation R2 of 0.9736±0.0053. Final evaluation results showed that there is strong accuracy, as depicted by R2 and MAPE values for NEXT, FEXT, and CMC of 0.9707/15.10%, 0.9602/11.11%, and 0.9750/8.19%, respectively, validating generalization performance.
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