AI RESEARCH
Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning
arXiv CS.LG
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ArXi:2606.02936v1 Announce Type: new In this manuscript, we propose and analyze hierarchical Kolmogoro--Arnold neural network architectures employing radial basis functions as activation functions for approximating deterministic functions and random field models. Specifically, we develop a hierarchical radial-basis-function Kolmogoro--Arnold network (hierarchical RBF-KAN) for multidimensional deterministic function approximation and a hierarchical radial-basis-function stochastic Kolmogoro--Arnold network (hierarchical RBF-SKAN) for random field learning.