November 30, 2020 to December 3, 2020
Southern Methodist University
America/Chicago timezone

Deep Learning Acceleration of Progress in Fusion Energy Research

Dec 1, 2020, 1:15 PM
Southern Methodist University

Southern Methodist University


Bill Tang (Princeton University)


Accelerated progress in delivering accurate predictions in science and industry have been accomplished by engaging advanced statistical methods featuring artificial intelligence/deep learning/machine learning (AI/DL/ML). Associated techniques have enabled new avenues of data-driven discovery in key scientific applications areas such as the quest to deliver Fusion Energy – identified by the 2015 CNN “Moonshots for the 21st Century” televised series as one of 5 prominent grand challenges for the world today. An especially time-urgent and challenging problem facing the development of a fusion energy reactor is the need to reliably predict and avoid large-scale major disruptions in magnetically-confined tokamak systems such as the EUROFUSION Joint European Torus (JET) today and the burning plasma ITER device in the near future -- -- a ground-breaking $25B international burning plasma experiment with the potential capability to exceed “breakeven” fusion power by a factor of 10 or more with “first plasma” targeted for 2026 in France. Meanwhile, a key challenge is to deliver significantly improved methods of prediction with better than 95% predictive accuracy to provide advanced warning for disruption avoidance/mitigation strategies to be effectively applied before critical damage can be done to ITER

This presentation describes advances in the deployment of deep learning recurrent and convolutional neural networks in Princeton’s Deep Learning Code -- "FRNN” – that have enabled the rapid analysis of large complex datasets on supercomputing systems that have accelerated progress in predicting tokamak disruptions with unprecedented accuracy and speed (Ref. “NATURE,” (April 26, 2019). This represented the first adaptable predictive DL software trained on leadership class systems to deliver accurate predictions for disruptions across different tokamak devices (DIII-D in the US and JET in the UK) – with the unique capability to carry out efficient “transfer learning” via training on a large data base from one experiment (i.e., DIII-D) and be able to accurately predict disruption onset on an unseen device (i.e., JET) ! Moreover, in recent advances, the FRNN inference engine has recently been deployed in a real-time plasma control system on the DIII-D tokamak facility in San Diego,CA. This opens up exciting avenues for moving from passive disruption prediction to active real-time control with subsequent optimization for reactor scenarios.

Presentation materials