Scientists at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory and Princeton University have created a machine learning framework designed to manage the rapid fluctuations that occur in fusion plasma systems. The framework, called PACMAN, addresses a fundamental challenge in fusion research: plasma instabilities can develop and disrupt fusion reactions in just milliseconds, a timescale beyond human reaction capability. The system was successfully tested in five separate experiments at the DIII-D National Fusion Facility and the results were published in Nuclear Fusion.
Fusion reactors such as tokamaks use powerful magnetic fields to contain extremely hot plasma and maintain the conditions necessary for fusion reactions. However, even minor disturbances in the plasma can rapidly destabilize the system. Traditional computer simulations used to predict plasma behavior require days or months to complete, making them impractical for real-time control during experiments. Machine learning models offer a solution by providing predictions and control decisions on the order of milliseconds—approximately 50 times faster than a focused human operator could respond.
PACMAN functions as an integrated platform that combines multiple artificial intelligence models into a coordinated control system. The framework collects live measurements from the tokamak, validates the data, feeds it to predictive models, and uses controller algorithms to determine necessary adjustments to heating systems, magnets, and other equipment. A final safety layer resolves conflicting instructions and enforces hardware limitations before sending commands to the machine. This modular design allows researchers to add or modify components without disrupting the entire system.
The testing demonstrated several practical advantages. In one experiment, machine learning models predicted a specific type of plasma instability approximately 200 milliseconds in advance, allowing the system to prevent it rather than suppress it after onset. The framework also successfully coordinated six simultaneous heating systems to achieve complex performance targets in real time. Researchers noted that implementing additional models into the system became progressively faster after the initial deployment, potentially accelerating the pace of fusion research.
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