IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction. (arXiv:2208.13197v1 [physics.plasm-ph])

Disruption prediction has made rapid progress in recent years, especially in
machine learning (ML)-based methods. Understanding why a predictor makes a
certain prediction can be as crucial as the prediction's accuracy for future
tokamak disruption predictors. The purpose of most disruption predictors is
accuracy or cross-machine capability. However, 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
and gives us …

arxiv disruption extraction feature idp physics