  This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent
breakthrough in artificial intelligence, for smart grid optimizations. To fully
leverage KAN's interpretability, a general framework is proposed considering
complex uncertainties. The stochastic optimal power flow problem in hybrid
AC/DC systems is chosen as a particularly tough case study for demonstrating
the effectiveness of this framework.
