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

arXiv: https://arxiv.org/abs/2410.08889 · HTML: https://mlsanity.com/paper/2410.08889/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)
- [A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2009.13935/) (2020)
- [Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices](https://mlsanity.com/paper/1911.00149/) (2019)

## Built on by (most cited first, 3 of 3)

- [XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion](https://mlsanity.com/paper/2502.05615/) (2025)
- [Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network](https://mlsanity.com/paper/2508.03776/) (2025)
- [Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking](https://mlsanity.com/paper/2606.26455/) (2026)

## Abstract (arXiv)

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.