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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16636 |
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| _version_ | 1866916857184780288 |
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| author | Zhou, Cuizhi Zhu, Kaien |
| author_facet | Zhou, Cuizhi Zhu, Kaien |
| contents | The equilibrium reconstruction of plasma is a core step in real-time diagnostic tasks in fusion research. This paper explores a multi-stage Physics-Informed Neural Networks(PINNs) approach to solve the Grad-Shafranov equation, achieving high-precision solutions with an error magnitude of $O(10^{-8})$ between the output of the second-stage neural network and the analytical solution. Our results demonstrate that the multi-stage PINNs provides a reliable tool for plasma equilibrium reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Zhou, Cuizhi Zhu, Kaien Plasma Physics The equilibrium reconstruction of plasma is a core step in real-time diagnostic tasks in fusion research. This paper explores a multi-stage Physics-Informed Neural Networks(PINNs) approach to solve the Grad-Shafranov equation, achieving high-precision solutions with an error magnitude of $O(10^{-8})$ between the output of the second-stage neural network and the analytical solution. Our results demonstrate that the multi-stage PINNs provides a reliable tool for plasma equilibrium reconstruction. |
| title | Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2507.16636 |