[
    {
        "id": "osp-21800",
        "type": "article-journal",
        "title": "Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation",
        "author": [
            {
                "family": "Rigas",
                "given": "Spyros"
            },
            {
                "family": "Papadopoulos",
                "given": "Theofilos"
            },
            {
                "family": "Alexandridis",
                "given": "Georgios"
            },
            {
                "family": "Antonopoulos",
                "given": "Antonios"
            }
        ],
        "URL": "https://omanscience.com/en/articles/explainable-machine-learning-for-multilayer-planar-winding-inductance-estimation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]