Accelerated Deep Learning Discovery in Fusion Energy Science

PI Name: 
William Tang
Institution: 
Princeton Plasma Physics Laboratory
Allocation Program: 
Aurora ESP
Year: 
2018
Research Domain: 
Physics

Machine learning and artificial intelligence can demonstrably accelerate scientific progress in predictive modeling for grand challenge areas such as the quest for clean energy via fusion power.  This project seeks to expand modern convolutional and recurrent neural net software to carry out optimized hyperparameter tuning on exascale supercomputers to make strides toward validated prediction and associated mitigation of large-scale disruptions in burning plasmas such as ITER.