Separable Hamiltonian Neural Networks
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910565598756864 |
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| author | Khoo, Zi-Yu Wu, Dawen Low, Jonathan Sze Choong Bressan, Stéphane |
| author_facet | Khoo, Zi-Yu Wu, Dawen Low, Jonathan Sze Choong Bressan, Stéphane |
| contents | Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding a bias regarding the additive separability of the Hamiltonian reduces the regression complexity and improves regression performance. We propose separable HNNs that embed additive separability within HNNs using observational, learning, and inductive biases. We show that the proposed models are more effective than the HNN at regressing the Hamiltonian and the vector field. Consequently, the proposed models predict the dynamics and conserve the total energy of the Hamiltonian system more accurately. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_01069 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Separable Hamiltonian Neural Networks Khoo, Zi-Yu Wu, Dawen Low, Jonathan Sze Choong Bressan, Stéphane Machine Learning Artificial Intelligence Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding a bias regarding the additive separability of the Hamiltonian reduces the regression complexity and improves regression performance. We propose separable HNNs that embed additive separability within HNNs using observational, learning, and inductive biases. We show that the proposed models are more effective than the HNN at regressing the Hamiltonian and the vector field. Consequently, the proposed models predict the dynamics and conserve the total energy of the Hamiltonian system more accurately. |
| title | Separable Hamiltonian Neural Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2309.01069 |