Deep Learning Acceleration of Progress toward Delivery of Fusion Energy
Event Type
Machine Learning Day
AI/Machine Learning/Deep Learning
Big Data Analytics
Extreme-Scale Computing
TimeWednesday, June 19th8:30am - 9:15am CEST
LocationPanorama 3
DescriptionAccelerated progress in delivering accurate predictions in science and industry have been accomplished by engaging advanced statistical methods featuring machine/deep learning/artificial intelligence (ML/DL/AI). 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. Significantly improved methods of prediction with better than 95% predictive accuracy are required to provide sufficient advanced warning for disruption avoidance/mitigation strategies to be effectively applied before critical damage can be done to ITER -- 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. This presentation will introduce high performance computing (HPC) relevant advances in the deployment of deep learning recurrent and convolutional neural networks in Princeton’s Deep Learning Code -- "FRNN” -- on top supercomputing systems worldwide that have accelerated progress in predicting tokamak disruptions with unprecedented accuracy and speed (Ref. “NATURE,” to be published, May 2019). Powerful current HPC systems engaged include SUMMIT in the US and ABCI in Japan.