| Overview of JET results for optimising ITER operation J Mailloux, N Abid, K Abraham, P Abreu, O Adabonyan, P Adrich, ... Nuclear Fusion 62 (4), 042026, 2022 | 105 | 2022 |
| Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions S Mazzi, J Garcia, D Zarzoso, YO Kazakov, J Ongena, M Dreval, ... Nature Physics 18 (7), 776-782, 2022 | 67 | 2022 |
| A sustained high-temperature fusion plasma regime facilitated by fast ions H Han, SJ Park, C Sung, J Kang, YH Lee, J Chung, TS Hahm, B Kim, ... Nature 609 (7926), 269-275, 2022 | 58 | 2022 |
| Feedforward beta control in the KSTAR tokamak by deep reinforcement learning J Seo, YS Na, B Kim, CY Lee, M Park, S Park, Y Lee Nuclear Fusion 61 (10), 106010, 2021 | 56 | 2021 |
| Disruption prediction with artificial intelligence techniques in tokamak plasmas J Vega, A Murari, S Dormido-Canto, GA Rattá, M Gelfusa, et al. Nature Physics 18 (7), 741-750, 2022 | 44 | 2022 |
| Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR J Seo, YS Na, B Kim, CY Lee, MS Park, SJ Park, YH Lee Nuclear Fusion 62 (8), 086049, 2022 | 31 | 2022 |
| Development of integrated suite of codes and its validation on KSTAR CY Lee, J Seo, SJ Park, JG Lee, SK Kim, B Kim, CS Byun, YS Lee, ... Nuclear Fusion 61 (9), 096020, 2021 | 26 | 2021 |
| Avoiding fusion plasma tearing instability with deep reinforcement learning J Seo, SK Kim, A Jalalvand, R Conlin, A Rothstein, J Abbate, K Erickson, ... Nature 626 (8000), 746-751, 2024 | 21 | 2024 |
| Solving real-world optimization tasks using physics-informed neural computing J Seo Scientific Reports 14 (1), 202, 2024 | 17 | 2024 |
| On benchmarking of simulations of particle transport in ITER YS Na, F Koechl, AR Polevoi, CS Byun, DH Na, J Seo, F Felici, ... Nuclear Fusion 59 (7), 076026, 2019 | 16 | 2019 |
| Observation of a new type of self-generated current in magnetized plasmas YS Na, J Seo, Y Lee, G Choi, M Park, S Park, S Yi, W Wang, MG Yoo, ... Nature Communications 13 (1), 6477, 2022 | 13 | 2022 |
| Parametric study of linear stability of toroidal Alfvén eigenmode in JET and KSTAR J Seo, J Kim, J Mailloux, RJ Dumont, M Fitzgerald, SE Sharapov, ... Nuclear Fusion 60 (6), 066008, 2020 | 10 | 2020 |
| Machine learning-based real-time kinetic profile reconstruction in DIII-D R Shousha, J Seo, K Erickson, A Xing, SK Kim, J Abbate, E Kolemen Nuclear Fusion 64 (2), 026006, 2024 | 8 | 2024 |
| Multimodal prediction of tearing instabilities in a tokamak J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, A Jalalvand, E Kolemen 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 8 | 2023 |
| Effect of electron cyclotron beam width to neoclassical tearing mode stabilization by minimum seeking control in ITER M Park, YS Na, J Seo, M Kim, K Kim Nuclear Fusion 58 (1), 016042, 2017 | 8 | 2017 |
| Ion heating by nonlinear Landau damping of high-n toroidal Alfvén eigenmodes in ITER J Seo, YS Na, TS Hahm Nuclear Fusion 61 (9), 096022, 2021 | 7 | 2021 |
| Investigation of performance enhancement by balanced double-null shaping in KSTAR B Kim, MS Park, YH Lee, SK Kim, CY Lee, SC Hong, J Seo, JG Lee, ... Nuclear Fusion 63 (12), 126013, 2023 | 3 | 2023 |
| Avoiding tokamak tearing instability with artificial intelligence E Kolemen, J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, K Erickson, ... | 3 | 2023 |
| Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing J Seo Physical Review E 110 (2), 025302, 2024 | 2 | 2024 |
| Disruption prediction and analysis through multimodal deep learning in KSTAR J Kim, J Lee, J Seo, Y Lee, YS Na Fusion Engineering and Design 200, 114204, 2024 | 2 | 2024 |
Yong-Su NaSeoul National University在 snu.ac.kr 的电子邮件经过验证
