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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書誌詳細

主要な著者:	Amirhossein Mollaali, Christian Bolivar Moya, Amanda A Howard, Alexander Heinlein, Panos Stinis, Guang Lin
フォーマット:	Artigo
言語:	Inglês
出版事項:	IOP Publishing 2026-01-01
シリーズ:	Machine Learning: Science and Technology
主題:	Kolmogorov–Arnold Networks (KANs) conformal prediction uncertainty quantification scientific machine learning
オンライン･アクセス:	https://doi.org/10.1088/2632-2153/ae7495
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