{"title":"Deep Learning Based Surrogate Model a fast Soft X-ray (SXR) Tomography on HL-2 a Tokamak","authors":"Zhijun Wang, Zeyu Zhang, Dong Li, Yixiong Wei, Zongyu Yang, Renjie Yang, Cong Wang, Yunbo Dong","doi":"10.1007/s10894-024-00419-6","DOIUrl":null,"url":null,"abstract":"<div><p>Tomography is indeed a commonly employed diagnostic technique in fusion campaigns, specifically for determining the shape and position of the plasma. To enhance the accuracy of conventional tomography algorithms, a Bayesian-based non-stationary Gaussian processes tomography as the emission model has been implemented in the soft X-ray diagnostics of the HL-2 A tokamak. However, the Bayesian tomography method is time-consuming and has difficulty achieving quick reconstructions for tokamak. In this work, neural networks have been trained and tested on a large set of sample tomograms based on experimental SXR data and Bayesian tomography method. The trained neural networks can predict the reconstructions of emission profiles accurately, fast, and robustly to noise. In the future, it is possible to easily implement this algorithm on different diagnostics and fusion devices.</p></div>","PeriodicalId":634,"journal":{"name":"Journal of Fusion Energy","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Fusion Energy","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10894-024-00419-6","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Tomography is indeed a commonly employed diagnostic technique in fusion campaigns, specifically for determining the shape and position of the plasma. To enhance the accuracy of conventional tomography algorithms, a Bayesian-based non-stationary Gaussian processes tomography as the emission model has been implemented in the soft X-ray diagnostics of the HL-2 A tokamak. However, the Bayesian tomography method is time-consuming and has difficulty achieving quick reconstructions for tokamak. In this work, neural networks have been trained and tested on a large set of sample tomograms based on experimental SXR data and Bayesian tomography method. The trained neural networks can predict the reconstructions of emission profiles accurately, fast, and robustly to noise. In the future, it is possible to easily implement this algorithm on different diagnostics and fusion devices.
层析成像确实是核聚变活动中常用的诊断技术，特别是用于确定等离子体的形状和位置。为了提高传统层析成像算法的准确性，HL-2 A 托卡马克的软 X 射线诊断中采用了基于贝叶斯的非稳态高斯过程层析成像作为发射模型。然而，贝叶斯层析成像法耗时较长，难以实现托卡马克的快速重建。在这项工作中，根据 SXR 实验数据和贝叶斯层析成像法，对大量层析成像样本集进行了神经网络训练和测试。训练有素的神经网络可以准确、快速、稳健地预测发射轮廓的重建。未来，这种算法有可能在不同的诊断和融合设备上轻松实现。
期刊介绍：
The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews.
This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.
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