Paper ID: 2410.08889

Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network

Qingchuan Ma, Shiao Wang, Tong Zheng, Xiaodong Dai, Yifeng Wang, Qingquan Yang, Xiao Wang

This study addresses the critical challenge of predicting the Q-distribution in long-term stable nuclear fusion task, a key component for advancing clean energy solutions. We introduce an innovative deep learning framework that employs Modern Hopfield Networks to incorporate associative memory from historical shots. Utilizing a newly compiled dataset, we demonstrate the effectiveness of our approach in enhancing Q-distribution prediction. The proposed method represents a significant advancement by leveraging historical memory information for the first time in this context, showcasing improved prediction accuracy and contributing to the optimization of nuclear fusion research.

Submitted: Oct 11, 2024