IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction

Abstract

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called Interpretable Disruption Predictor based on Physics-Guided Feature Extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (TPR = 97.27%, FPR = 5.45%, AUC = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible means of inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also give a possible experimental analysis direction that the RMPs delay the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.

Cross-tokamak disruption prediction based on domain adaptation

“…However, in the context of disruption, True Positive (TP) and False Negative (FN) have specific way to be defined. For J-TEXT, any predicted disruption with a warning time of more than 10 ms is considered TP (less than 10 ms is considered FN) [24]. For EAST, any predicted disruption with a warning time of more than 30 ms is considered TP (less than 30 ms is considered FN) [52].…”

Recent progress on deep learning-based disruption prediction algorithm in HL-2A tokamak

“…[23] In Subsection 5.1, a new method called device adversarial neural network (DANN) is tried and performs well when it is trained on HL-2A, J-TEXT and adapted to HL-2M. The other routine is to train a reliable algorithm with as fewer training data as possible, with the help of physical prior knowledge, some examples has been implemented in DIII-D [24] and J-TEXT. [25] Subsections 5.2 and 5.3 propose two possible physical inductive biases, i.e., plasma equilibrium equation and disruption related instabilities, that could be integrated into the disruption prediction algorithm.…”

Enhancing disruption prediction through Bayesian neural network in KSTAR

“…There are two distinguished ways to predict disruptions in fusion research: physics-based approach [7][8][9] and datadriven approach [5,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The former has good interpretability and physical consistency for predicting disruptions through physical models combined with MHD theory.…”