Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913541982781440 |
|---|---|
| author | Ma, Qingchuan Wang, Shiao Zheng, Tong Dai, Xiaodong Wang, Yifeng Yang, Qingquan Wang, Xiao |
| author_facet | Ma, Qingchuan Wang, Shiao Zheng, Tong Dai, Xiaodong Wang, Yifeng Yang, Qingquan Wang, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08889 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network Ma, Qingchuan Wang, Shiao Zheng, Tong Dai, Xiaodong Wang, Yifeng Yang, Qingquan Wang, Xiao Computer Vision and Pattern Recognition 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. |
| title | Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.08889 |