Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction

Cuizhi Zhou; Kaien Zhu

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arXiv · 2507.16636

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction

Cuizhi Zhou·Kaien Zhu

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Abstract

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.

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Cuizhi Zhou, Kaien Zhu. 2025-07-22. Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction. https://arxiv.org/abs/2507.16636

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