AI System Enhances Safety and Efficiency of Fusion Reactors

Scientists at the Hefei Institute of Physical Science at the Chinese Academy of Sciences have developed two innovative AI systems to enhance the safety and efficiency of fusion energy experiments. The research team introduced two distinct AI-driven solutions to address critical challenges in fusion reactor operations. First, they designed an interrupt prediction system based on an interpretable decision tree model. This system is specifically aimed at identifying early warning signals of disruptions, particularly those caused by "locked modes," a common type of plasma instability. Unlike typical black-box AI models, this approach not only provides predictive results but also offers in-depth analysis of the physical signals that trigger warnings. Second, the team created a plasma state monitoring tool using a multi-task learning model. This advanced AI solution can simultaneously identify operating modes (such as L-mode and H-mode) and detect edge localized modes (ELMs). Compared to traditional single-task models, this system significantly improves processing speed and accuracy. It achieved a 96.7% success rate in real-time plasma condition classification, thereby enhancing the reliability of continuous reactor operation. These AI advancements underscore the team's commitment to making fusion energy safer and more efficient, positioning them at the forefront of nuclear fusion research. The systems are expected to play a crucial role in advancing the field, as global competition intensifies to develop sustainable and safe fusion reactors. Meta’s strategic investment in AI for such applications could also see benefits from these developments, contributing to broader AI-driven advancements in scientific research. However, the focus here is primarily on the Chinese research team's contributions and the potential impact of their work on the future of fusion energy. Their work can be found at https://phys.org/news/2025-07-ai-advances-boost-safety-fusion.html.