The high acquisition cost and the significant demand for disruptive discharges for data-
driven disruption prediction models in future tokamaks pose an inherent contradiction in …

The present research focuses on investigating deep neural networks techniques for
predicting plasma disruptions in tokamaks. For this purpose, various deep-learning …

In this research, we develop a data-driven disruption predictor based on Bayesian deep
probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR …

Introduction: This work explores the use of eXplainable artificial intelligence (XAI) to analyze
a convolutional neural network (CNN) trained for disruption prediction in tokamak devices …

Predicting the remaining time before the next earthquake based on seismic signals
generated in a laboratory setting is a challenging research task that is of significant …