5 citations · 5 across the 1 of their papers we have counts for
3 papers
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…
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…