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

TL;DR

This paper presents a novel deep learning framework using Modern Hopfield Networks to incorporate historical memory for improved Q-distribution prediction in nuclear fusion, advancing clean energy research.

Contribution

It introduces the first application of associative memory via Modern Hopfield Networks for Q-distribution prediction in nuclear fusion.

Findings

Enhanced prediction accuracy demonstrated on a new dataset.

Effective integration of historical shot data improves model performance.

Contributes to optimization efforts in nuclear fusion research.

Abstract

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.