Fusion energy promises clean, virtually inexhaustible power, but future reactors must operate reliably to prevent damaging disruptions and maintain precise plasma confinement. Disruptions are sudden, intense events that threaten reactor integrity, while maintaining high-performance confinement states is critical for sustained operation.
To address these issues, the team built two specialized AI platforms. The first, a disruption predictor, employs decision tree models to identify early warning signals of disruptions triggered by locked modes, a common plasma instability. Unlike opaque black-box algorithms, this model is interpretable, pointing to the physical causes behind its predictions. In trials, it successfully issued warnings 94 percent of the time, with alerts arriving 137 milliseconds before the disruption-enough time for countermeasures.
The second AI system focuses on real-time plasma monitoring. Instead of relying on separate models to classify confinement states such as L-mode and H-mode and to detect edge-localized modes (ELMs), the researchers developed a multi-task learning framework that performs both simultaneously. This approach increased both accuracy and resilience, achieving a 96.7 percent success rate in recognizing plasma conditions.
Together, these innovations advance the prospects of next-generation fusion reactors by boosting safety, improving performance, and contributing to deeper insights into plasma dynamics.
Research Report:Automatic identification of tokamak plasma confinement states (L-mode, ELM-free H-mode, and ELMy H-mode) with multi-task learning neural network
Related Links
Hefei Institutes of Physical Science Chinese Academy of Sciences
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