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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

TL;DR

An innovative deep learning framework that employs Modern Hopfield Networks to incorporate associative memory from historical shots is introduced that represents a significant advancement by leveraging historical memory information for the first time in this context.

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.

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

TL;DR

An innovative deep learning framework that employs Modern Hopfield Networks to incorporate associative memory from historical shots is introduced that represents a significant advancement by leveraging historical memory information for the first time in this context.

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.

Paper Structure

This paper contains 13 sections, 7 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Our Proposed Approach
  3. Problem Formulation
  4. Overview
  5. Input Processing
  6. Network Architecture
  7. Loss Function
  8. Experiments
  9. Dataset and Evaluation Metric
  10. Implementation Details
  11. Comparison with Other Algorithms
  12. Ablation Study
  13. Conclusion

Figures (4)

  • Figure 1: An overview of our proposed associative memory augmented Q-distribution using the Modern Hopfield Networks.
  • Figure 2: Impact of Hidden Size on Hopfield Network for Historical Samples
  • Figure 3: Ablation studies on the number of heads of MHN for historical samples.
  • Figure 4: Ablation studies on the number of layers of Hopfield Network for historical samples.