Nonlinear model reduction for operator learning
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914730482860032 |
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| author | Eivazi, Hamidreza Wittek, Stefan Rausch, Andreas |
| author_facet | Eivazi, Hamidreza Wittek, Stefan Rausch, Andreas |
| contents | Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field. Recently, an extension of DeepONet based on model reduction and neural networks, proper orthogonal decomposition (POD)-DeepONet, has been able to outperform other architectures in terms of accuracy for several benchmark tests. We extend this idea towards nonlinear model order reduction by proposing an efficient framework that combines neural networks with kernel principal component analysis (KPCA) for operator learning. Our results demonstrate the superior performance of KPCA-DeepONet over POD-DeepONet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonlinear model reduction for operator learning Eivazi, Hamidreza Wittek, Stefan Rausch, Andreas Machine Learning Numerical Analysis Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field. Recently, an extension of DeepONet based on model reduction and neural networks, proper orthogonal decomposition (POD)-DeepONet, has been able to outperform other architectures in terms of accuracy for several benchmark tests. We extend this idea towards nonlinear model order reduction by proposing an efficient framework that combines neural networks with kernel principal component analysis (KPCA) for operator learning. Our results demonstrate the superior performance of KPCA-DeepONet over POD-DeepONet. |
| title | Nonlinear model reduction for operator learning |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2403.18735 |