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Tuesday, September 13, 2022

Reinforcement Learning Aids Fusion Control

Linking AI Methods to  Nuclear fusion energy. 

Exploring Reinforcement Learning to Control Nuclear Fusion Reactions

Carnegie Mellon University News

Aaron Aupperlee, September 8, 2022

Carnegie Mellon University (CMU) doctoral candidate Ian Char, the first CMU researcher to run an experiment on the DIII-D National Fusion Facility's tokamak machine, demonstrated that reinforcement learning algorithms can control the rotation of the machine's hydrogen plasma. Char developed two algorithms: one was trained using data from the tokamak on how the plasma reacts, while the other calculates the rate and direction at which to add hydrogen particles to affect the speed of the plasma's rotation. Said CMU's Jeff Schneider, "This work shows a path to using reinforcement learning to control other parts of the plasma state and ultimately achieve the temperatures and pressures long enough to have a power plant. That would mean limitless, clean energy for everyone."

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