用历史数据提升核聚变能量分布预测精度
Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network
- 用现代霍普菲尔德网络提取历史实验记忆
- 在新数据集上实现更高预测准确率
- 适合核聚变研究与深度学习交叉方向
本研究针对长期稳定核聚变任务中能量约束因子（Q-distribution）预测的关键挑战，提出一种创新的深度学习框架。该框架首次在该领域引入历史实验数据的关联记忆机制，利用现代霍普菲尔德网络（Modern Hopfield Network）建模历史射线（shots）中的模式信息。基于新构建的数据集，实验验证了该方法在提升预测精度方面的有效性。结果表明，通过融合历史记忆信息，模型显著改善了对Q-distribution的预测性能，为优化核聚变研究提供了新的技术路径。
原文摘要 · Abstract (English)
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.
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