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cs.LG2025★ 7 cited
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…
cs.LG2024★ 52 cited
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…