Deep neural networks for plasma tomography with applications to JET and COMPASS

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F19%3A00522747" target="_blank" >RIV/61389021:_____/19:00522747 - isvavai.cz</a>
Result on the web
<a href="https://iopscience.iop.org/article/10.1088/1748-0221/14/09/C09011/pdf" target="_blank" >https://iopscience.iop.org/article/10.1088/1748-0221/14/09/C09011/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1748-0221/14/09/C09011" target="_blank" >10.1088/1748-0221/14/09/C09011</a>

Result language
angličtina
Original language name
Deep neural networks for plasma tomography with applications to JET and COMPASS
Original language description
Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)

Project
<a href="/en/project/LM2015045" target="_blank" >LM2015045: COMPASS – Tokamak for Thermonuclear Fusion Research</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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