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Full-Pulse Tomographic Reconstruction with Deep Neural Networks
Culham Sci Ctr, JET, EUROfus Consortium, Abingdon, Oxon, England; Univ Lisbon, Inst Super Tecn, Inst Plasmas & Fusao Nucl, Lisbon, Portugal.ORCID iD: 0000-0001-5818-9406
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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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
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:uu:diva-398202DOI: 10.1080/15361055.2017.1390386ISI: 000436997000006OAI: oai:DiVA.org:uu-398202DiVA, id: diva2:1375240
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
Funder
EU, Horizon 2020, 633053
Note

For complete list of authors see http://dx.doi.org/10.1080/15361055.2017.1390386

Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-04Bibliographically approved

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias

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Ferreira, Diogo R.Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias
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