Deep learning for Gaussian process soft x-ray tomography model selection in the ASDEX Upgrade tokamak-Reference-Cited by-同舟云学术

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Deep learning for Gaussian process soft x-ray tomography model selection in the ASDEX Upgrade tokamak

Published:2020-10-01 Issue:10 Volume:91 Page:103501
ISSN:0034-6748
Container-title:Review of Scientific Instruments
language:en
Short-container-title:Review of Scientific Instruments

Author:

Matos F.¹^ORCID,Svensson J.²,Pavone A.²^ORCID,Odstrčil T.³^ORCID,Jenko F.¹^ORCID

Affiliation:

1. Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching, Germany

2. Max Planck Institute for Plasma Physics, Wendelsteinstr. 1, 17491 Greifswald, Germany

3. Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

Funder

FP7 Fusion Energy Research

Publisher

AIP Publishing

Subject

Instrumentation

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0020680

Reference41 articles.

1. Chapter 7: Tomography Diagnostics: Bolometry and Soft-X-Ray Detection

2. Inversion Techniques in the Soft-X-Ray Tomography of Fusion Plasmas: Toward Real-Time Applications

3. Optimized tomography methods for plasma emissivity reconstruction at the ASDEX Upgrade tokamak

4. Current Research into Applications of Tomography for Fusion Diagnostics

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1. Validating and speeding up x-ray tomographic inversions in tokamak plasmas;Plasma Physics and Controlled Fusion;2024-07-03

2. A Gaussian process guide for signal regression in magnetic fusion;Nuclear Fusion;2024-02-09

3. Estimation of plasma parameter profiles and their derivatives from linear observations by using Gaussian processes;Plasma Physics and Controlled Fusion;2023-11-06

4. Plasma diagnostics using fast cameras at the GOLEM tokamak;Fusion Engineering and Design;2023-08

5. MHD mode tracking using high-speed cameras and deep learning;Plasma Physics and Controlled Fusion;2023-05-30

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