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

0522747 - ÚFP 2020 RIV GB eng J - Journal Article
Carvalho, D. D. - Ferreira, D. R. - Carvalho, P. J. - Imríšek, Martin - Mlynář, Jan - Fernandes, H.
Deep neural networks for plasma tomography with applications to JET and COMPASS.
Journal of Instrumentation. Roč. 14, č. 9 (2019), č. článku C09011. ISSN 1748-0221. E-ISSN 1748-0221
R&D Projects: GA MŠMT(CZ) LM2015045
EU Projects: European Commission(XE) 633053 - EUROfusion
Institutional support: RVO:61389021
Keywords : Computerized Tomography (CT) and Computed Radiography (CR) * Plasma diagnostics-interferometry, spectroscopy and imaging
OECD category: Fluids and plasma physics (including surface physics)
Impact factor: 1.454, year: 2019
Method of publishing: Limited access
https://iopscience.iop.org/article/10.1088/1748-0221/14/09/C09011/pdf

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.
Permanent Link: http://hdl.handle.net/11104/0307190