| Overview of the SPARC tokamak AJ Creely, MJ Greenwald, SB Ballinger, D Brunner, J Canik, J Doody, ... Journal of Plasma Physics 86 (5), 865860502, 2020 | 298 | 2020 |
| A real-time machine learning-based disruption predictor in DIII-D C Rea, KJ Montes, KG Erickson, RS Granetz, RA Tinguely Nuclear Fusion 59 (9), 096016, 2019 | 94 | 2019 |
| Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST KJ Montes, C Rea, RS Granetz, RA Tinguely, N Eidietis, OM Meneghini, ... Nuclear Fusion 59 (9), 096015, 2019 | 89 | 2019 |
| Disruption prediction investigations using machine learning tools on DIII-D and Alcator C-Mod C Rea, RS Granetz, K Montes, RA Tinguely, N Eidietis, JM Hanson, ... Plasma Physics and Controlled Fusion 60 (8), 084004, 2018 | 89 | 2018 |
| Progress in disruption prevention for ITER EJ Strait, JL Barr, M Baruzzo, JW Berkery, RJ Buttery, PC de Vries, ... Nuclear Fusion 59 (11), 112012, 2019 | 73 | 2019 |
| Overview of the SPARC physics basis towards the exploration of burning-plasma regimes in high-field, compact tokamaks P Rodriguez-Fernandez, AJ Creely, MJ Greenwald, D Brunner, ... Nuclear Fusion 62 (4), 042003, 2022 | 63 | 2022 |
| Exploratory machine learning studies for disruption prediction using large databases on DIII-D C Rea, RS Granetz Fusion Science and Technology 74 (1-2), 89-100, 2018 | 61 | 2018 |
| New structures in the continuum of 15C populated by two-neutron transfer F Cappuzzello, C Rea, A Bonaccorso, M Bondì, D Carbone, M Cavallaro, ... Physics Letters B 711 (5), 347-352, 2012 | 53 | 2012 |
| Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks JX Zhu, C Rea, K Montes, RS Granetz, R Sweeney, RA Tinguely Nuclear Fusion 61 (2), 026007, 2020 | 48 | 2020 |
| MHD stability and disruptions in the SPARC tokamak R Sweeney, AJ Creely, J Doody, T Fülöp, DT Garnier, R Granetz, ... Journal of Plasma Physics 86 (5), 865860507, 2020 | 44 | 2020 |
| Disruption prediction on EAST tokamak using a deep learning algorithm BH Guo, DL Chen, B Shen, C Rea, RS Granetz, L Zeng, WH Hu, JP Qian, ... Plasma Physics and Controlled Fusion 63 (11), 115007, 2021 | 38 | 2021 |
| Overview of the RFX-mod fusion science activity M Zuin, S Dal Bello, L Marrelli, ME Puiatti, P Agostinetti, M Agostini, ... Nuclear Fusion 57 (10), 102012, 2017 | 38 | 2017 |
| Progress toward interpretable machine learning–based disruption predictors across tokamaks C Rea, KJ Montes, A Pau, RS Granetz, O Sauter Fusion Science and Technology 76 (8), 912-924, 2020 | 36 | 2020 |
| Disruption prediction using a full convolutional neural network on EAST BH Guo, B Shen, DL Chen, C Rea, RS Granetz, Y Huang, L Zeng, ... Plasma Physics and Controlled Fusion 63 (2), 025008, 2020 | 32 | 2020 |
| Real-time prediction of high-density EAST disruptions using random forest WH Hu, C Rea, QP Yuan, KG Erickson, DL Chen, B Shen, Y Huang, ... Nuclear Fusion 61 (6), 066034, 2021 | 31 | 2021 |
| An application of survival analysis to disruption prediction via Random Forests RA Tinguely, KJ Montes, C Rea, R Sweeney, RS Granetz Plasma Physics and Controlled Fusion 61 (9), 095009, 2019 | 29 | 2019 |
| 3D effects on the RFX-mod boundary N Vianello, G Spizzo, M Agostini, P Scarin, L Carraro, R Cavazzana, ... Nuclear Fusion 53 (7), 073025, 2013 | 27 | 2013 |
| Development and experimental qualification of novel disruption prevention techniques on DIII-D JL Barr, B Sammuli, DA Humphreys, E Olofsson, XD Du, C Rea, ... Nuclear Fusion 61 (12), 126019, 2021 | 25 | 2021 |
| Overview of the RFX-mod contribution to the international Fusion Science Program ME Puiatti, S Dal Bello, L Marrelli, P Martin, P Agostinetti, M Agostini, ... Nuclear Fusion 55 (10), 104012, 2015 | 24 | 2015 |
| Magnetic perturbations as a viable tool for edge turbulence modification N Vianello, C Rea, M Agostini, R Cavazzana, G Ciaccio, G De Masi, ... Plasma Physics and Controlled Fusion 57 (1), 014027, 2014 | 24 | 2014 |
Roy Alexander TinguelyPostdoctoral Associate, MIT PSFCVerified email at mit.edu
