الملخص
Rapid and accurate self-inductance estimation for multilayer rectangle-shaped planar windings is essential for modern high-frequency power converters, yet traditional workflows rely on complex mathematical equations, rigid monomial formulas or unexplainable black-box machine learning (ML) models that degrade severely outside their training domain. This paper introduces an explainable ML framework unifying post-hoc feature attribution (SHAP and permutation importance) with Kolmogorov-Arnold Network-guided symbolic regression via the SR-KAN framework to discover closed-form analytical equations without prior structural assumptions. Evaluated on a new open-source dataset of over 10,000 Finite Element Analysis (FEA) simulations across seven out-of-distribution (OOD) classes, standard tree-based ensembles exhibit severe extrapolation errors (> 36%), whereas the unconstrained SR-KAN expression achieves a robust OOD relative error of 8.22%. Experimental verification across 55 physical printed circuit board prototypes (up to 8 layers, with inductances from 4.11 μH to 559.27 μH) confirms that the KAN-discovered expression translates effectively to real-world hardware, predicting inductance with a mean absolute relative error of 6.26%. To support reproducible research, the complete FEA simulation dataset and prototype measurements are released open-source.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Rigas, S., Papadopoulos, T., Alexandridis, G., & Antonopoulos, A. (2026). Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation. https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation
MLA 9
Rigas, Spyros, et al. "Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation." https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation.
شيكاغو (المؤلف–التاريخ)
Rigas, Spyros, Theofilos Papadopoulos, Georgios Alexandridis, and Antonios Antonopoulos. 2026. "Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation." https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation.
هارفارد
Rigas, S., Papadopoulos, T., Alexandridis, G. and Antonopoulos, A. (2026) 'Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation', Available at: https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation.
فانكوفر
Rigas S, Papadopoulos T, Alexandridis G, Antonopoulos A. Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation. https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation
IEEE
S. Rigas, T. Papadopoulos, G. Alexandridis, and A. Antonopoulos, "Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation," https://omanscience.com/ar/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation.