Conformalized-KANs: uncertainty quantification with coverage guarantees for Kolmogorov–Arnold Networks (KANs) in scientific machine learning

This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov–Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. However, particularly in data-limited a...

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Hauptverfasser:	Amirhossein Mollaali, Christian Bolivar Moya, Amanda A Howard, Alexander Heinlein, Panos Stinis, Guang Lin
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IOP Publishing 2026-01-01
Schriftenreihe:	Machine Learning: Science and Technology
Schlagworte:	Kolmogorov–Arnold Networks (KANs) conformal prediction uncertainty quantification scientific machine learning
Online-Zugang:	https://doi.org/10.1088/2632-2153/ae7495
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