An artist's sketch and a cross-section view of the DIII-D tokamak. (Images: General Atomics)

At the DIII-D National Fusion Facility near San Diego, Calif., home to the largest operating tokamak in North America, researchers from Thomas Jefferson National Accelerator Facility worked to develop a machine learning framework capable of adaptively predicting changes in a tokamak’s hardware.

The team’s research was recently published in the journal Machine Learning with Applications.

Research with the Department of Energy’s Thomas Jefferson National Accelerator Facility (Jefferson Lab) has revealed new insights into short-range correlations—the brief pairings of nucleons (protons with neutrons, protons with protons, or neutrons with neutrons) in the nuclei of atoms. The study, published in Nature, used precision measurements to determine that short-range correlations differ depending on the density of the nucleus, that is, how many nucleons it contains.