Scientists at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory and Princeton University have created a software framework designed to address one of fusion energy’s most pressing challenges: the need to make control decisions faster than human operators can physically react. The framework, known as PACMAN, uses artificial intelligence to manage plasma instabilities that can develop in just milliseconds within fusion reactors called tokamaks. Researchers successfully tested the system across five experiments and published their findings in Nuclear Fusion.
Fusion reactors maintain energy through the confinement of extremely hot, charged gas using powerful magnetic fields. However, plasma can become unstable within thousandths of a second, and even small disturbances can disrupt the entire fusion reaction if not corrected immediately. Traditional computer simulations that predict plasma behavior are valuable for planning but require days or months to complete, making them unsuitable for real-time control during experiments that last only minutes. Machine learning models, by contrast, can analyze plasma behavior and make decisions in the millisecond timeframe required for active control.
PACMAN was specifically designed to integrate multiple machine learning models into a unified control system that operates continuously in a repeating loop structure. The framework functions as a pipeline with distinct stages: collecting live measurements from the tokamak, validating the data, using AI models to predict plasma conditions, determining necessary control actions, and applying safety limits before sending commands to equipment. By operating in approximately 20 milliseconds repeatedly, the system responds to plasma changes far more rapidly than human operators, who typically require seconds to react.
During testing at the DIII-D National Fusion Facility, PACMAN demonstrated several capabilities. In one experiment, machine learning models predicted a specific type of plasma instability about 200 milliseconds in advance, allowing operators to adjust conditions preventatively rather than react after disruption occurred. The system also successfully coordinated six separate heating systems simultaneously in real time, optimizing their power output and physical positioning to meet preset targets. Researchers noted that the framework’s modular design made it significantly faster to add new AI components—what initially took months for the first model required only days for subsequent additions.
The system maintains human oversight throughout its operation, with researchers setting objectives and safety parameters while the AI handles rapid decision-making beyond human reaction capability. This division of responsibility aims to preserve human control over fusion experiments while leveraging machine learning’s speed advantage for real-time plasma management.
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