Learning state-space models with Kolmogorov-Arnold networks: an autonomous underwater engineering perspective.
This paper investigates Kolmogorov-Arnold Networks (KANs) as parametrisations of learned nonlinear state-space models in control-relevant settings. We ask whether replacing standard MLP maps with KAN-based ones yields a useful trade-off between predictive accuracy and deployability-oriented properties such as compactne...