Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas

Abstract

The helium I line intensity ratio (LIR) method is used to measure the electron density (n_e) and temperature (T_e) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict n_e and T_e, recent studies have shown that machine learning approaches can provide better measurements if a sufficient dataset for training is available. This study investigates a hybrid neural network approach that combines CRM- and experiment-based models. Although the CRM-based model alone exhibited negative transfer in most cases, the ensemble model modestly improved the prediction accuracy of T_e. Notably, in data-limited scenarios, the CRM-based model outperformed the others for T_e prediction, highlighting its potential for applications with constrained diagnostic access.