Learning symbolic models of dynamical systems through Kolmogorov–Arnold Networks (KANs) in centralized and distributed settings

Identifying an interpretable and tractable model is a crucial step for the analysis and control of dynamical systems. In this work, we employ the recently introduced Kolmogorov–Arnold Networks (KANs), a novel neural network architecture tailored for symbolic regression and interpretability, to learn...

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Bibliografski detalji

Glavni autori:	Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa
Format:	Artigo
Jezik:	Inglês
Izdano:	KeAi Communications Co., Ltd. 2026-06-01
Serija:	Journal of Automation and Intelligence
Teme:	Model identification Kolmogorov–Arnold Networks (KANs) Data-driven systems Symbolic regression
Online pristup:	http://www.sciencedirect.com/science/article/pii/S2949855425000681
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