GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks

Nisal Ranasinghe, Yu Xia, Sachith Seneviratne +1

Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exi…