The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) to solve one of humankind’s most pressing issues: generating clean, reliable energy from fusing plasma.
Unlike traditional computer code, machine learning — a type of artificially intelligent software — isn’t simply a list of instructions. Machine learning is software that can analyze data, infer relationships between features, learn from this new knowledge, and adapt. PPPL researchers believe this ability to learn and adapt could improve their control over fusion reactions in various ways. This includes perfecting the design of vessels surrounding the super-hot plasma, optimizing heating methods, and maintaining stable control of the reaction for increasingly long periods.
The Lab’s artificial intelligence research is already yielding significant results. In a new paper published in Nature Communications, PPPL researchers explain how they used machine learning to avoid magnetic perturbations, or disruptions, which destabilize fusion plasma.
“The results are particularly impressive because we were able to achieve them on two different tokamaks using the same code,” said PPPL Staff Research Physicist SangKyeun Kim, the lead author of the paper. A tokamak is a donut-shaped device that uses magnetic fields to hold a plasma.
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