Achieving a sustained fusion reaction is a delicate balancing act, requiring a sea of moving parts to come together to maintain a high-performing plasma: one that is dense enough, hot enough, and confined for long enough for fusion to take place.

Yet as researchers push the limits of plasma performance, they have encountered new challenges for keeping plasmas under control, including one that involves bursts of energy escaping from the edge of a super-hot plasma. These edge bursts negatively impact overall performance and even damage the plasma-facing components of a reactor over time.

Now, a team of fusion researchers led by engineers at Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress these harmful edge instabilities — without sacrificing plasma performance.

“Not only did we show our approach was capable of maintaining a high-performing plasma without instabilities, but we also showed that it can work at two different facilities,” said research leader Egemen Kolemen, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. “We demonstrated that our approach is not just effective — it’s versatile as well.”

Kolemen said the current work is yet another example of the potential for AI to overcome long-standing bottlenecks in developing fusion power as a clean energy resource.