Predicting Disruptions in Magnetic Confinement Fusion Reactors Via Deep Learning at the Largest Scale

Julian Kates-Harbeck, Harvard University

Photo of Julian Kates-Harbeck

Nuclear fusion power via magnetic confinement tokamak reactors carries the promise of sustainable and clean energy production. The avoidance of large-scale plasma instabilities called disruptions represents the most pressing obstacle to this goal. Disruptions are particularly deleterious for large reactors such as the multi-billion dollar international ITER project currently under construction, which for the first time aims to produce more energy than it consumes. Here, we present a new approach to forecast disruptions based on deep learning that for the first time overcomes the crucial limitations of past work, such as first- principles based and classical machine-learning approaches. In particular, our method for the first time (i) delivers reliable predictions on machines other than the one it was trained on – a crucial requirement for large future reactors that cannot afford "training" disruptions; (ii) utilizes high-dimensional training data such as profiles to boost predictive performance; and (iii) engages supercomputing at the largest scale to deliver solutions with unprecedented accuracy and speed. Trained on experimental data from the largest tokamaks in the United States (DIII-D) and the world (JET), our method moreover provides new insights into fusion physics and associated discovery science, can be tuned for physics-specific tasks such as prediction with long warning times, and opens up promising avenues for advancing the goal from passive disruption prediction to active reactor control and optimization.

Abstract Author(s): Julian Kates-Harbeck, Alexey Svyatkovskiy, William Tang