A comprehensive study of the uncertainties in bolometric tomography on JET using the maximum likelihood method-Reference-Cited by-同舟云学术

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A comprehensive study of the uncertainties in bolometric tomography on JET using the maximum likelihood method

Published:2019-12-01 Issue:12 Volume:90 Page:123502
ISSN:0034-6748
Container-title:Review of Scientific Instruments
language:en
Short-container-title:Review of Scientific Instruments

Author:

Peluso E.¹^ORCID,Craciunescu T.²^ORCID,Murari A.³^ORCID,Carvalho P.⁴,Gelfusa M.¹^ORCID,

Affiliation:

1. Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, Roma 00133, Italy

2. National Institute for Laser, Plasma and Radiation Physics, Magurele-Bucharest RO-077125, Romania

3. Consorzio RFX (CNR, ENEA, INFN, Universita’ di Padova, Acciaierie Venete SpA), Corso Stati, Padova 35127, Italy

4. Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Lisbon 1049-001, Portugal

Funder

FP7 Fusion Energy Research

Publisher

AIP Publishing

Subject

Instrumentation

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5119441

Reference18 articles.

1. On the determination of functions from their integral values along certain manifolds

2. Soft X ray tomography during ELMs and impurity injection in JET

3. Full-Pulse Tomographic Reconstruction with Deep Neural Networks

4. Current Research into Applications of Tomography for Fusion Diagnostics

5. Maximum likelihood bolometric tomography for the determination of the uncertainties in the radiation emission on JET TOKAMAK

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors;Nature Communications;2024-03-18

2. A hybrid physics/data-driven logic to detect, classify, and predict anomalies and disruptions in tokamak plasmas;Nuclear Fusion;2024-02-27

3. Maximum likelihood bolometry for ASDEX upgrade experiments;Physica Scripta;2023-11-09

4. On the potential of physics-informed neural networks to solve inverse problems in tokamaks;Nuclear Fusion;2023-11-07

5. A systematic investigation of radiation collapse for disruption avoidance and prevention on JET tokamak;Matter and Radiation at Extremes;2023-06-13

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