SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks

ArXi:2603.18548v1 Announce Type: new Kolmogoro-Arnold networks (KANs) have arisen as a potential way to enhance the interpretability of machine learning. However, solutions learned by KANs are not necessarily interpretable, in the sense of being sparse or parsimonious. Sparse identification of nonlinear dynamics (SINDy) is a complementary approach that allows for learning sparse equations for dynamical systems from data; however, learned equations are limited by the library.