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.
@article{arxiv.2410.08889,
title = {Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network},
author = {Qingchuan Ma and Shiao Wang and Tong Zheng and Xiaodong Dai and Yifeng Wang and Qingquan Yang and Xiao Wang},
journal= {arXiv preprint arXiv:2410.08889},
year = {2024}
}