Deep learning for plasma tomography using the bolometer system at JET

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• 作者：Francisco A. Matos^a ; Diogo R. Ferreira^a ; ^{diogo.ferreira@tecnico.ulisboa.pt} ; Pedro J. Carvalho^b ; JET Contributors¹ ; EUROfusion Consortium ; JET ; Culham Science Centre ; Abingdon ; OX14 3DB ; UK
• 关键词：Plasma diagnostics ; Computed tomography ; Neural networks ; Deep learning
• 刊名：Fusion Engineering and Design
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：114
• 期：Complete
• 页码：18-25
• 全文大小：661 K
• 卷排序：114

文摘

Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight. The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model. Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms. We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions. After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%.