
Graphic courtesy of the Princeton Plasma Physics Laboratory.
New artificial intelligence (AI) models for plasma heating can do more than was previously thought possible, not only increasing the prediction speed 10 million times while preserving accuracy but also correctly predicting plasma heating in cases where the original numerical code failed. The models were presented on Oct. 11 at the 66th Annual Meeting of the American Physical Society Division of Plasma Physics in Atlanta.
“With our intelligence, we can train the AI to go even beyond the limitations of available numerical models,” said Álvaro Sánchez Villar, an associate research physicist at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL). Sánchez Villar is the lead author on a new peer-reviewed journal article in Nuclear Fusion about the work. It was part of a project that spanned five research institutions.
The models use machine learning, a type of AI, to try to predict the way electrons and ions in a plasma behave when ion cyclotron range of frequency (ICRF) heating is applied in fusion experiments. The models are trained on data generated by a computer code. While much of the data agreed with past results, in some extreme scenarios, the data wasn’t what they expected.