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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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
KeAi Communications Co., Ltd.
2026-06-01
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| Serie: | Journal of Automation and Intelligence |
| Soggetti: | |
| Accesso online: | http://www.sciencedirect.com/science/article/pii/S2949855425000681 |
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