| 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 |
| 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 |
| Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks J Zhu, C Rea, RS Granetz, ES Marmar, KJ Montes, R Sweeney, ... Nuclear Fusion 61 (11), 114005, 2021 | 22 | 2021 |
| A semi-supervised machine learning detector for physics events in tokamak discharges KJ Montes, C Rea, RA Tinguely, R Sweeney, J Zhu, RS Granetz Nuclear Fusion 61 (2), 026022, 2021 | 19 | 2021 |
| Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas JX Zhu, C Rea, RS Granetz, ES Marmar, R Sweeney, K Montes, ... Nuclear Fusion 63 (4), 046009, 2023 | 8 | 2023 |
| Observation of a beam-driven low-frequency mode in Heliotron J LG Zang, S Yamamoto, DA Spong, K Nagasaki, S Ohshima, S Kobayashi, ... Nuclear Fusion 59 (5), 056001, 2019 | 3 | 2019 |
| Data-driven study of major disruption prediction and plasma instabilities across multiple tokamaks J Zhu Massachusetts Institute of Technology, 2023 | 1 | 2023 |
| Hybrid deep learning architecture for general disruption prediction across tokamaks (vol 61, 026007, 2021) JX Zhu, C Rea, K Montes, RS Granetz, R Sweeney, RA Tinguely NUCLEAR FUSION 61 (4), 2021 | 1 | 2021 |
| Development of a correlation ECE radiometer for electron temperature fluctuation measurements in Heliotron J GM Weir, K Nagasaki, J Zhu, M Luo, H Okada, T Minami, S Kado, ... EPJ Web of Conferences 203, 03013, 2019 | 1 | 2019 |
| Empirical probability and machine learning analysis of m, n= 2, 1 tearing mode onset parameter dependence in DIII-D H-mode scenarios L Bardóczi, NJ Richner, J Zhu, C Rea, NC Logan Physics of Plasmas 30 (9), 2023 | | 2023 |
| Root Cause of Disruptive NTMs in DIII-D ITER Baseline Scenario Plasma L Bardoczi, N Richner, N Logan, J Zhu, C Rea, E Strait APS Division of Plasma Physics Meeting Abstracts 2023, TI01. 001, 2023 | | 2023 |
| Overview of SPARC disruption prediction and avoidance research C Rea, P Kaloyannis, Z Keith, A Maris, A Saperstein, L Spangher, ... APS Division of Plasma Physics Meeting Abstracts 2023, JP11. 120, 2023 | | 2023 |
| Preparing for Disruptions in the SPARC Q> 1 Campaign R Sweeney, D Battaglia, A Battey, S Benjamin, T Body, J Boguski, ... APS Division of Plasma Physics Meeting Abstracts 2023, JP11. 119, 2023 | | 2023 |
| Do Fusion Plasma Time-Series Have a Persistent Memory that Machine Learning May Exploit? L Spangher, J Zhu, C Rea, A Spangher, W Arnold, M Bonotto, F Cannarile, ... APS Division of Plasma Physics Meeting Abstracts 2023, JP11. 121, 2023 | | 2023 |
| Interpretable Machine Learning Accelerating Fusion Research C Rea, J Zhu, R Granetz, K Montes, R Tinguely, R Sweeney, N Howard, ... APS Division of Plasma Physics Meeting Abstracts 2022, CT02. 001, 2022 | | 2022 |
| Disruption Research for SPARC C Rea, R Sweeney, R Tinguely, R Granetz, D Garnier, B Stein-Lubrano, ... APS Division of Plasma Physics Meeting Abstracts 2022, NO03. 007, 2022 | | 2022 |
| Empirical boundary detection of tearing mode onset at DIII-D J Zhu, C Rea, R Granetz, E Marmar, R Sweeney, F Turco, K Erickson, ... APS Division of Plasma Physics Meeting Abstracts 2022, GP11. 035, 2022 | | 2022 |
| Disruption physics driving the SPARC design R Sweeney, V Riccardo, D Garnier, R Granetz, M Greenwald, A Maris, ... APS Division of Plasma Physics Meeting Abstracts 2021, JO07. 005, 2021 | | 2021 |
| Accelerating Disruption Database Studies with Semi-Supervised Learning K Montes, R Granetz, J Zhu, C Rea | | 2020 |
