Artificial intelligence latest news: Control fusion experiment

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy(link is external) that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U) -- the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma(link is external) that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas -- largely self-heating fusion reactions -- that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper(link is external) in Nuclear Fusion that describes the machine learning tactics.