Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities

ArXi:2410.04096v2 Announce Type: replace-cross In this paper, we propose to use Sinc interpolation in the context of Kolmogoro-Arnold Networks, neural networks with learnable activation functions, which recently gained attention as alternatives to Multilayer Perceptron. Many different function representations have already been tried, but we show that Sinc interpolation proposes a viable alternative, since it is known in numerical analysis to effectively represent both smooth functions and functions with singularities.