The instability prediction of non-resonant energetic particle modes based on machine learning algorithms

Abstract Non-resonant energetic particle modes (EPMs) are extremely common in tokamak experimental phenomenon, which can disrupt the plasma balance, subsequently reducing the device confinement performance. Nevertheless, it should be stressed that the hybrid simulation of EPMs requires considerable time and computational resources in the study of such phenomena. To solve this issue, this research proposes the Machine Learning (ML) approaches to predict the linear instability of non-resonant EPMs. Here, the current study compares four single model algorithms in order to select the optimal one for predicting the non-resonant EPMs instability, including the Decision Tree Regression (DT), K-Nearest Neighbor (KNN) regression, Support vector Regression (SVR) and Multilayer perceptron (MLP). By optimizing the hyper-parameters for these models, the final evaluation results reveal quite high accuracy and R 2 values, all around 0.9. Moreover, this work examines the influence of missing different feature variables on the model evaluation. Through this comparison, the study analyzes the degree of importance of various factors in forecasting the instability of high-energy particle modes.