Quantifying and Propagating Uncertainties to Enhance Real-time Disruption Prediction with Machine Learning

Having the capability to predict disruptions in tokamak reactors has the potential to dramatically improve/optimize both the performance and operating/repair costs of these reactors. Practical disruption prediction in tokamaks has recently improved by utilizing advanced analytics via data-driven machine learning algorithms that utilize real-time machine diagnostics to predict the onset of major disruptions. In this talk we discuss various ways to enhance these predictive capabilities by incorporating and propagating experimental uncertainties from diagnostic signals into the more conventional machine learning algorithms.