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

TL;DR

This paper introduces a multi-stage Physics-Informed Neural Network approach to accurately solve the Grad-Shafranov equation for plasma equilibrium reconstruction, achieving high precision with minimal error.

Contribution

The paper presents a novel multi-stage PINNs method that significantly improves the accuracy of plasma equilibrium reconstruction compared to existing techniques.

Findings

Achieved an error magnitude of O(10^{-8}) with the neural network.

Demonstrated the reliability of PINNs for real-time plasma equilibrium reconstruction.

Provided a high-precision solution method for the Grad-Shafranov equation.

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.