Researchers are increasingly turning to artificial intelligence to enhance the safety and efficiency of nuclear fusion reactors, particularly tokamak devices, which are essential for advancing fusion energy technology. A recent study led by L. Bonalumi from the Department of Physics at the Università degli Studi Milano Bicocca explores the application of eXplainable Artificial Intelligence (XAI) to improve disruption prediction in these complex systems.
Disruptions in tokamak devices can lead to significant operational challenges, including damage to equipment and interruptions in plasma confinement. The study focuses on a convolutional neural network (CNN) trained to recognize patterns in disruptions based on variations in the electron temperature profile. By analyzing a reduced dataset of disruptions, the researchers aimed to determine whether the CNN could differentiate between disruption types without being explicitly trained for these specific scenarios.
Bonalumi’s team implemented two XAI algorithms—occlusion and saliency maps—to uncover how the CNN interprets the temperature profile data. The findings revealed that the CNN’s sensitivity to disruptions shifts based on whether the inner or outer regions of the temperature profile are affected. This suggests that the model has implicitly learned to recognize the underlying physical phenomena occurring within the plasma, which could be crucial for predicting and mitigating disruptions in real-time.
“The main outcome of this paper comes from the temperature profile analysis, which evaluates whether the CNN prioritizes the outer and inner regions,” Bonalumi stated. This capability to predict disruptions more accurately could lead to enhanced operational stability in tokamak devices, making fusion energy a more viable option for large-scale energy production.
The implications of this research extend beyond the laboratory. As the demand for clean and sustainable energy sources grows, the ability to harness fusion energy effectively could open up new commercial opportunities. Energy companies and technology developers focused on nuclear fusion can leverage these advanced AI techniques to improve the reliability and efficiency of fusion reactors, potentially leading to reduced costs and increased investment in the sector.
The study was published in the journal “Frontiers in Physics,” highlighting the ongoing efforts to integrate cutting-edge technology with nuclear fusion research. As the field continues to evolve, the insights gained from applying XAI to disruption prediction may play a significant role in the future of energy production.
