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arXiv · 2604.20085

A Physics-Informed Neural Network for Solving the Quasi-static Magnetohydrodynamic Equations

Jonathan S. Arnaud·Christopher J. McDevitt·Golo Wimmer·Xian-Zhu Tang
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Abstract

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any experimental or synthetic data. The initial study considered an ITER-like tokamak and found that a PINN, after careful treatment, was capable of learning the solution to the MHD system and predict a vertically displacing plasma, where general agreement with ground truth simulation was observed. The proof-of-principle demonstration highlights the potential of physics-constrained deep learning to learn complex plasma behavior.

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Jonathan S. Arnaud, Christopher J. McDevitt, Golo Wimmer, Xian-Zhu Tang. 2026-04-22. A Physics-Informed Neural Network for Solving the Quasi-static Magnetohydrodynamic Equations. https://arxiv.org/abs/2604.20085

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