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 have been extensively utilized in discovering Deep Koopman operators for linearizing nonlinear dynamics. With the emergence of Kolmogorov-Arnold Networks (KANs) as a more efficient and accurate alternative to the MLP Neural Network%2C we propose a comparison of the performance of each network type in the context of learning Koopman operators with control. In this work%2C we propose a KANs-based deep Koopman framework with applications to an orbital Two-Body Problem (2BP) and the pendulum for data-driven discovery of linear system dynamics. KANs were found to be superior in nearly all aspects of training%3B learning 31 times faster%2C being 15 times more parameter efficiency%2C and predicting 1.25 times more accurately as compared to the MLP Deep Neural Networks (DNNs) in the case of the 2BP. Thus%2C KANs shows potential for being an efficient tool in the development of Deep Koopman Theory.?quality=80&w=800)
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