A team of scientists at the US Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are applying deep learning to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions.
“This research opens a promising new chapter in the effort to bring unlimited energy to Earth,” said Steve Cowley, director of PPPL
“AI is exploding across the sciences, and now it’s beginning to contribute to the worldwide quest for fusion power,” he added in the current issue of Nature magazine.
The deep learning code also opens possible pathways for controlling as well as predicting disruptions.
With AI, “we’ve accelerated the ability to predict with high accuracy the most dangerous challenge to clean fusion energy,” added Bill Tang, a principal research physicist at PPPL.
Unlike traditional software, which carries out prescribed instructions, deep learning learns from its mistakes.
Accomplishing this seeming magic are neural networks, layers of interconnected nodes — mathematical algorithms — that are weighted by the programme to shape the desired output.
The next step will be to move from prediction to the control of disruptions.
“Rather than predicting disruptions at the last moment and then mitigating them, we would ideally use future deep learning models to gently steer the plasma away from regions of instability with the goal of avoiding most disruptions in the first place,” said collaborator Julian Kates-Harbeck, a physics graduate student at Harvard University.