Deep learning based plasma response models to 3D external magnetic field perturbations in EAST

The real-time response monitoring model and the physics feature based response optimization model are proposed on EAST using three deep learning models. Compared with the widely used fully connected neural network (FNN) and convolutional neural network—long short-term memory (CNN–LSTM) models, the inverted transformer (iTransformer) model, which uses the attention mechanism to more naturally capture multivariate dependencies, demonstrates the best overall performance. For real-time response monitoring, the iTransformer model is trained on 133157 experimental samples with 4-fold toroidal phase symmetry augmentation. This model establishes a direct nonlinear mapping from resonant magnetic perturbation (RMP) coils and electromagnetic measurement sensors to the plasma response. With an inference time per discharge of less than , it can provide real-time response monitoring under flexible three-dimensional (3D) fields. For experimental response optimization, the physics feature based iTransformer model is trained on 12080 filtered experimental samples. This model captures the dependence of the response amplitude on key parameters, including , , , , , , and . Combined with SHapley Additive exPlanations (SHAP) analysis, it provides interpretable insights into parameter influences and directly guides the optimization of 3D field experimental discharges. This work establishes a comprehensive framework for real-time monitoring and physics feature based optimization of plasma responses based on statistical patterns in large datasets, thereby paving the way for active control of error-field locked mode and edge localized mode (ELM).