Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed Neural Networks
<|reference_start|>Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed Neural Networks: In this paper, we propose to use Sinc interpolation in the context of Kolmogorov-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 represent well both smooth functions and functions with singularities. This is important not only for function approximation but also for the solutions of partial differential equations with physics-informed neural networks. Through a series of experiments, we show that SincKANs provide better results in almost all of the examples we have considered.<|reference_end|>
@article{yu2024sinc,
    title={Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed
  Neural Networks},
    author={Tianchi Yu, Jingwei Qiu, Jiang Yang, Ivan Oseledets},
    journal={arXiv preprint arXiv:2410.04096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04096},
    primaryClass={cs.LG cs.AI cs.NA cs.NE math.NA physics.comp-ph}
}