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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.19994 |
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| _version_ | 1866929734210813952 |
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| author | Xu, Baige Tanaka, Yusuke Matsubara, Takashi Yaguchi, Takaharu |
| author_facet | Xu, Baige Tanaka, Yusuke Matsubara, Takashi Yaguchi, Takaharu |
| contents | In recent years, deep learning for modeling physical phenomena which can be described by partial differential equations (PDEs) have received significant attention. For example, for learning Hamiltonian mechanics, methods based on deep neural networks such as Hamiltonian Neural Networks (HNNs) and their variants have achieved progress. However, existing methods typically depend on the discretization of data, and the determination of required differential operators is often necessary. Instead, in this work, we propose an operator learning approach for modeling wave equations. In particular, we present a method to compute the variational derivatives that are needed to formulate the equations using the automatic differentiation algorithm. The experiments demonstrated that the proposed method is able to learn the operator that defines the Hamiltonian density of waves from data with unspecific discretization without determination of the differential operators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Hamiltonian Density Using DeepONet Xu, Baige Tanaka, Yusuke Matsubara, Takashi Yaguchi, Takaharu Machine Learning In recent years, deep learning for modeling physical phenomena which can be described by partial differential equations (PDEs) have received significant attention. For example, for learning Hamiltonian mechanics, methods based on deep neural networks such as Hamiltonian Neural Networks (HNNs) and their variants have achieved progress. However, existing methods typically depend on the discretization of data, and the determination of required differential operators is often necessary. Instead, in this work, we propose an operator learning approach for modeling wave equations. In particular, we present a method to compute the variational derivatives that are needed to formulate the equations using the automatic differentiation algorithm. The experiments demonstrated that the proposed method is able to learn the operator that defines the Hamiltonian density of waves from data with unspecific discretization without determination of the differential operators. |
| title | Learning Hamiltonian Density Using DeepONet |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.19994 |