Physics-Informed Visual MARFE Prediction on the HL-3 Tokamak

TL;DR

This paper introduces a physics-informed neural network framework for early prediction of MARFE plasma instabilities in tokamaks, combining label refinement and continuous-time modeling to improve accuracy and real-time deployment.

Contribution

It presents a novel physics-informed indicator using a label refinement pipeline and Neural ODEs for short-horizon MARFE prediction in tokamaks, enhancing early warning capabilities.

Findings

Achieved an AUC of 0.969 for 40ms-ahead prediction.

Successfully deployed the predictor for real-time operation at 1 ms updates.

Demonstrated high predictive accuracy on HL-3 experimental data.

Abstract

The Multifaceted Asymmetric Radiation From the Edge (MARFE) is a critical plasma instability that often precedes density-limit disruptions in tokamaks, posing a significant risk to machine integrity and operational efficiency. Early and reliable alert of MARFE formation is therefore essential for developing effective disruption mitigation strategies, particularly for next-generation devices like ITER. This paper presents a novel, physics-informed indicator for early MARFE prediction and disruption warning developed for the HL-3 tokamak. Our framework integrates two core innovations: (1) a high-fidelity label refinement pipeline that employs a physics-scored, weighted Expectation-Maximization (EM) algorithm to systematically correct noise and artifacts in raw visual data from cameras, and (2) a continuous-time, physics-constrained Neural Ordinary Differential Equation (Neural ODE) model that predicts the short-horizon ``worsening" of a MARFE. By conditioning the model's dynamics on key plasma parameters such as normalized density ($f_G$, derived from core electron density) and core electron temperature ($T_e$), the predictor achieves superior performance in the low-false-alarm regime crucial for control. On a large experimental dataset from HL-3, our model demonstrates high predictive accuracy, achieving an Area Under the Curve (AUC) of 0.969 for 40ms-ahead prediction. The indicator has been successfully deployed for real-time operation with updates every 1 ms. This work lays a very foundation for future proactive MARFE mitigation.