image: Álvaro Sánchez Villar, an associate research physicist at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory, has developed new AI models for plasma heating that increase the prediction speed while preserving accuracy and providing accurate predictions where original numerical codes failed.
Credit: Michael Livingston / PPPL Communications Department
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 will be presented on October 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.
“We observed a parametric regime in which the heating profiles featured erratic spikes in rather arbitrary locations,” said Sánchez-Villar. “There was nothing physical to explain those spikes.”
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 will be presented on October 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.
“We observed a parametric regime in which the heating profiles featured erratic spikes in rather arbitrary locations,” said Sánchez-Villar. “There was nothing physical to explain those spikes.”
“This means that, practically, our surrogate implementation was equivalent to fixing the original code, just based on a careful curation of the data,” said Sánchez-Villar. “As with every technology, with an intelligent use, AI can help us solve problems not only faster, but better than before, and overcome our own human constraints.”
As expected, the models also improved the computation times for ICRF heating. Those times fell from roughly 60 seconds to 2 microseconds, enabling faster simulations without notably impacting the accuracy. This improvement will help scientists and engineers explore the best ways to make fusion a practical power source.
Other researchers on the project include Zhe Bai, Nicola Bertelli, E. Wes Bethel, Julien Hillairet, Talita Perciano, Syun’ichi Shiraiwa, Gregory M. Wallace and John C. Wright. The work was supported by the U.S. Department of Energy under Contract Number DE-AC02-09CH11466. This research used resources of the National Energy Research Scientific Computing Center (NERSC) operated under Contract No. DE-AC02-05CH11231 using NERSC Award FES m3716 for 2023.