Multifidelity Kolmogorov-Arnold Networks
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929550405926912 |
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| author | Howard, Amanda A. Jacob, Bruno Stinis, Panos |
| author_facet | Howard, Amanda A. Jacob, Bruno Stinis, Panos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14764 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multifidelity Kolmogorov-Arnold Networks Howard, Amanda A. Jacob, Bruno Stinis, Panos Machine Learning Numerical Analysis 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. |
| title | Multifidelity Kolmogorov-Arnold Networks |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2410.14764 |