Disruption Prediction in Tokamak Fusion Reactors via Deep Learning at Scale

Tue, Dec 11, 2018, 4:30 pm
Center for Statistics and Machine Learning
Department of Astrophysical Sciences

The prediction and avoidance of large-scale plasma instabilities called “disruptions” is a crucial step towards successful power generation from magnetic confinement fusion in tokamaks. Inspired by the recent success of deep learning (DL) algorithms in generalizing and learning from multi-modal and high-dimensional data across diverse domains, we present a new approach to forecast disruptions based on convolutional and recurrent neural networks. Our method for the first time overcomes the key limitations of past work in that it (i) delivers reliable predictions on machines other than the one it was trained on — a crucial requirement for large and powerful future reactors that cannot afford “training” disruptions; (ii) utilizes high-dimensional training data such as profiles to add new physics information and boost predictive performance; and (iii) engages supercomputing at the largest scale to deliver solutions with improved accuracy and speed. Trained on experimental data from the largest tokamaks in the US (DIII-D) and the world (JET), our algorithm moreover can be tuned for physics-specific tasks such as prediction with long warning times, and opens up possible avenues for moving forward the goal from passive disruption prediction to active reactor control and optimization.

Julian Kates-Harbeck received his bachelor’s degree in physics at Stanford and earned a master’s in computer science (with a focus on artificial intelligence and machine learning) at the same university. Before entering graduate school, he co-founded a tech startup, where he was responsible for product, hiring, and strategy. He is currently pursuing a PhD in physics at Harvard, studying dynamics on complex social and biological networks. He also works on various scientific challenge problems that can be impacted by -- and drive the development of -- state of the art methods in artificial intelligence.