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arXiv · 2410.14764

Multifidelity Kolmogorov-Arnold Networks

Amanda A. Howard·Bruno Jacob·Panos Stinis
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Abstract

We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-fidelity model along with a small amount of high-fidelity data to train a model for the high-fidelity data accurately. Multifidelity KANs (MFKANs) reduce the amount of expensive high-fidelity data needed to accurately train a KAN by exploiting the correlations between the low- and high-fidelity data to give accurate and robust predictions in the absence of a large high-fidelity dataset. In addition, we show that multifidelity KANs can be used to increase the accuracy of physics-informed KANs (PIKANs), without the use of training data.

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Amanda A. Howard, Bruno Jacob, Panos Stinis. 2024-10-18. Multifidelity Kolmogorov-Arnold Networks. https://arxiv.org/abs/2410.14764

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