Adversarial Autoencoders in Operator Learning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912151790157824 |
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| author | Enyeart, Dustin Lin, Guang |
| author_facet | Enyeart, Dustin Lin, Guang |
| contents | DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning. In this paper, the use an adversarial addition for these two neural operator architectures is studied. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07811 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Autoencoders in Operator Learning Enyeart, Dustin Lin, Guang Machine Learning DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning. In this paper, the use an adversarial addition for these two neural operator architectures is studied. |
| title | Adversarial Autoencoders in Operator Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.07811 |