  The development of Kolmogorov-Arnold networks (KANs) marks a significant
shift from traditional multi-layer perceptrons in deep learning. Initially,
KANs employed B-spline curves as their primary basis function, but their
inherent complexity posed implementation challenges. Consequently, researchers
have explored alternative basis functions such as Wavelets, Polynomials, and
Fractional functions. In this research, we explore the use of rational
functions as a novel basis function for KANs. We propose two different
approaches based on Pade approximation and rational Jacobi functions as
trainable basis functions, establishing the rational KAN (rKAN). We then
evaluate rKAN's performance in various deep learning and physics-informed tasks
to demonstrate its practicality and effectiveness in function approximation.
