Loss Terms and Operator Forms of Koopman Autoencoders
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909417849487360 |
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| author | Enyeart, Dustin Lin, Guang |
| author_facet | Enyeart, Dustin Lin, Guang |
| contents | Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presents a fair and systemic study of these options. Furthermore, it introduces novel loss terms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Loss Terms and Operator Forms of Koopman Autoencoders Enyeart, Dustin Lin, Guang Machine Learning Computational Physics Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presents a fair and systemic study of these options. Furthermore, it introduces novel loss terms. |
| title | Loss Terms and Operator Forms of Koopman Autoencoders |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2412.04578 |