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

The helium I line intensity ratio (LIR) method is used to measure the electron density (ne) and temperature (Te) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict ne and Te, 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 Te. Notably, in data-limited scenarios, the CRM-based model outperformed the others for Te prediction, highlighting its potential for applications with constrained diagnostic access.