52 citations · 68 across the 6 of their papers we have counts for
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Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation
Spyros Rigas, Theofilos Papadopoulos, Georgios Alexandridis +1
Rapid and accurate self-inductance estimation for multilayer rectangle-shaped planar windings is essential for modern high-frequency power converters, yet traditional workflows rel…
A Dynamic Framework for Grid Adaptation in Kolmogorov-Arnold Networks
Spyros Rigas, Thanasis Papaioannou, Panagiotis Trakadas +1
Kolmogorov-Arnold Networks (KANs) have recently demonstrated promising potential in scientific machine learning, partly due to their capacity for grid adaptation during training. H…
Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou +1
Since their introduction, Kolmogorov-Arnold Networks (KANs) have been successfully applied across several domains, with physics-informed machine learning (PIML) emerging as one of…
Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Spyros Rigas, Dhruv Verma, Georgios Alexandridis +1
Kolmogorov-Arnold Networks (KANs) are a recently introduced neural architecture that replace fixed nonlinearities with trainable activation functions, offering enhanced flexibility…
Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks
Spyros Rigas, Michalis Papachristou, Ioannis Sotiropoulos +1
Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the sam…
Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Michalis Papachristou, Theofilos Papadopoulos +2
Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks a…