52 citations · 68 across the 6 of their papers we have counts for
8 papers
Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs
Manousos Linardakis, Georgios Alexandridis
Background music that matches the mood of a text has been shown to make readers feel more immersed and improve their reading experience, motivating recommender systems that pair bo…
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…
An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks
Spyros Rigas, Ioannis Contopoulos, Georgios Alexandridis +1
The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach and treating the separatrix a…
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…