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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00294 |
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| _version_ | 1866913409492058112 |
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| author | He, Qing Cai, Wei |
| author_facet | He, Qing Cai, Wei |
| contents | We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of the phase space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Networks with Symplectic Preservation Properties He, Qing Cai, Wei Numerical Analysis Machine Learning Computational Physics 37J11, 70H15, 68T07 We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of the phase space. |
| title | Deep Neural Networks with Symplectic Preservation Properties |
| topic | Numerical Analysis Machine Learning Computational Physics 37J11, 70H15, 68T07 |
| url | https://arxiv.org/abs/2407.00294 |