Articolo in rivista, 2019, ENG, 10.1088/1748-0221/14/09/C09011

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

Carvalho D.D.; Ferreira D.R.; Carvalho P.J.; Imrisek M.; Mlynar J.; Fernandes H.; Jet Contributors

EUROfusion Consortium, Culham Science Centre, Abingdon, OX14 3DB, EUROfusion Consortium, JET, Culham Science Centre, Abingdon, OX14 3DB, United Kingdom; Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, 1049-001, Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal; Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, 1049-001, Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal; Institute of Plasma Physics AS CR, Prague, Institute of Plasma Physics AS CR, Prague, Czech Republic; et al.

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.

Journal of instrumentation 14 (9), pp. 1–6

Keywords

Computerized Tomography (CT) and Computed Radiography (CR), Plasma diagnostics - interferometry spectroscopy and imaging

CNR authors

Rigamonti Davide, Schmuck Stefan, Brunetti Daniele, Mariani Alberto, Murari Andrea, Pomaro Nicola, Sozzi Carlo, Taliercio Cesare, Ghezzi Francesco Mauro, Gervasini Gabriele, Innocente Paolo, Vianello Nicola, Predebon Italo, Terranova David, Piovesan Paolo, Figini Lorenzo, Bonfiglio Daniele, Brombin Matteo, Lazzaro Enzo, Nowak Silvana, Laguardia Laura, Perelli Cippo Enrico, Ricci Daria, Alessi Edoardo, Giacomelli Luca Carlo, Puiatti Maria Ester, Paccagnella Roberto, Causa Federica, Rebai Marica, Muraro Andrea, Uccello Andrea, Valisa Marco, Marchetto Chiara, Tardocchi Marco, Carraro Lorella, Mantica Paola, Manduchi Gabriele, Pasqualotto Roberto

CNR institutes

IFP – Istituto di fisica del plasma "Piero Caldirola", IGI – Istituto gas ionizzati, ISC – Istituto dei sistemi complessi, ISTP – Istituto per la Scienza e Tecnologia dei Plasmi