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Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model
Eindhoven Univ Technol, Dept Mech Engn, Control Syst Technol Grp, POB 513, NL-5600 MB Eindhoven, Netherlands.;Ecole Polytech Fed Lausanne, SPC, CH-1015 Lausanne, Switzerland..
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.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.
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Number of Authors: 12292018 (English)In: Nuclear Fusion, ISSN 0029-5515, E-ISSN 1741-4326, Vol. 58, no 9, article id 096006Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2018. Vol. 58, no 9, article id 096006
Keywords [en]
integrated tokamak simulation, real-time control, machine learning, tokamak profiles, tokamak transport
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-271134DOI: 10.1088/1741-4326/aac8f0ISI: 000437412100006Scopus ID: 2-s2.0-85051239087OAI: oai:DiVA.org:kth-271134DiVA, id: diva2:1417243
Note

QC 20200327

Available from: 2020-03-27 Created: 2020-03-27 Last updated: 2020-04-15Bibliographically approved

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Bergsåker, HenrikBykov, IgorFrassinetti, LorenzoGarcia Carrasco, AlvaroHellsten, TorbjörnJohnson, ThomasMenmuir, SPetersson, PerRachlew, ElisabethRatynskaia, SvetlanaRubel, MarekStefániková, EsteraStröm, PetterTholerus, EmmiTolias, PanagiotisOlivares, Pablo VallejosWeckmann, Armin
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