Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915087596388352 |
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| author | Zhao, Yequan Xiao, Xian Descos, Antoine Yuan, Yuan Yu, Xinling Kurczveil, Geza Fiorentino, Marco Zhang, Zheng Beausoleil, Raymond G. |
| author_facet | Zhao, Yequan Xiao, Xian Descos, Antoine Yuan, Yuan Yu, Xinling Kurczveil, Geza Fiorentino, Marco Zhang, Zheng Beausoleil, Raymond G. |
| contents | Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back-propagation-free on-photonic-chip training of physics-informed neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00742 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training Zhao, Yequan Xiao, Xian Descos, Antoine Yuan, Yuan Yu, Xinling Kurczveil, Geza Fiorentino, Marco Zhang, Zheng Beausoleil, Raymond G. Machine Learning Hardware Architecture Optics Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back-propagation-free on-photonic-chip training of physics-informed neural networks. |
| title | Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training |
| topic | Machine Learning Hardware Architecture Optics |
| url | https://arxiv.org/abs/2501.00742 |