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Full-Pulse Tomographic Reconstruction with Deep Neural Networks
Culham Sci Ctr, JET, EUROfus Consortium, Abingdon OX14 3DB, Oxon, England.;Univ Lisbon, Inst Super Tecn, Inst Plasmas & Fusao Nucl, P-1049001 Lisbon, Portugal..ORCID iD: 0000-0001-5818-9406
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.ORCID iD: 0000-0002-9546-4494
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Number of Authors: 12252018 (English)In: Fusion science and technology, ISSN 1536-1055, E-ISSN 1943-7641, Vol. 74, no 1-2, p. 47-56Article in journal (Refereed) Published
Abstract [en]

Plasma tomography consists of reconstructing a two-dimensional radiation profile of a poloidal cross section of a fusion device based on line-integrated measurements along several lines of sight. The reconstruction process is computationally intensive, and in practice, only a few reconstructions are usually computed per pulse. In this work, we trained a deep neural network based on a large collection of sample tomograms that have been produced at JET over several years. Once trained, the network is able to reproduce those results with high accuracy. More importantly, it can compute all the tomographic reconstructions for a given pulse in just a few seconds. This makes it possible to visualize several phenomena-such as plasma heating, disruptions, and impurity transport-over the course of the entire pulse.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS INC , 2018. Vol. 74, no 1-2, p. 47-56
Keywords [en]
Plasma tomography, deep learning, convolutional neural networks
National Category
Fusion, Plasma and Space Physics
Identifiers
URN: urn:nbn:se:kth:diva-270533DOI: 10.1080/15361055.2017.1390386ISI: 000436997000006Scopus ID: 2-s2.0-85048073739OAI: oai:DiVA.org:kth-270533DiVA, id: diva2:1415105
Conference
2nd International Atomic Energy Agency (IAEA) Technical Meeting (TM) on Fusion Data Processing, Validation, and Analysis (IAEA-TM), MAY 30-JUN 02, 2017, Massachusetts Inst Technol Campus, Samberg Conf Ctr, Cambridge, MA
Note

QC 20200317

Available from: 2020-03-17 Created: 2020-03-17 Last updated: 2020-05-11Bibliographically approved

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Ferreira, Diogo R.Bykov, IgorFrassinetti, LorenzoGarcia-Carrasco, AlvaroHellsten, TorbjörnJohnson, ThomasMenmuir, SheenaPetersson, PerRachlew, ElisabethRatynskaia, SvetlanaRubel, MarekStefanikova, EsteraStröm, PetterTholerus, EmmiTolias, PanagiotisOlivares, Pablo VallejosWeckmann, ArminZhou, Yushun
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