Performance Prediction of Hall Thrusters with Deep Learning Algorithms

Accurate performance prediction is essential for improving the reliability and service life of Hall thrusters in space applications. Because conventional scaling-based prediction methods are often limited when the operating state and magnetic-field structure become complex, this study develops a deep-learning framework for rapid prediction of key Hall-thruster performance indicators. Three representative neural-network architectures, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM), are constructed and comparatively evaluated. Among them, BiLSTM achieves the best overall performance, with a Mean Absolute Percentage Error (MAPE) of 6.44% on the test set. Additional validation based on real Hall-thruster experimental data gives a MAPE of 3.884%, indicating that the proposed model has good generalization ability and can provide an effective data-driven tool for Hall-thruster performance prediction.