| Physics research on the TCV tokamak facility: from conventional to alternative scenarios and beyond S Coda, M Agostini, R Albanese, S Alberti, E Alessi, S Allan, J Allcock, ... Nuclear Fusion 59 (11), 112023, 2019 | 82 | 2019 |
| DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy ME Fenstermacher, J Abbate, S Abe, T Abrams, M Adams, B Adamson, ... Nuclear Fusion 62 (4), 042024, 2022 | 24 | 2022 |
| Time-dependent experimental identification of inter-ELM microtearing modes in the tokamak edge on DIII-D AO Nelson, FM Laggner, A Diallo, D Smith, ZA Xing, R Shousha, ... Nuclear Fusion 61 (11), 116038, 2021 | 22 | 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 |
| Optimization of 3D controlled ELM-free state with recovered global confinement for KSTAR with n= 1 resonant magnetic field perturbation SK Kim, R Shousha, SH Hahn, AO Nelson, J Wai, SM Yang, JK Park, ... Nuclear Fusion 62 (2), 026043, 2022 | 15 | 2022 |
| Design and experimental demonstration of feedback adaptive RMP ELM controller toward complete long pulse ELM suppression on KSTAR R Shousha, SK Kim, KG Erickson, SH Hahn, AO Nelson, SM Yang, ... Physics of Plasmas 29 (3), 2022 | 10 | 2022 |
| Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR M Kim, G Shin, J Lee, WH Ko, H Han, SH Hahn, SK Kim, SM Yang, ... Nuclear Fusion 63 (8), 086032, 2023 | 8 | 2023 |
| Machine learning-based real-time kinetic profile reconstruction in DIII-D R Shousha, J Seo, K Erickson, Z Xing, SK Kim, J Abbate, E Kolemen Nuclear Fusion 64 (2), 026006, 2023 | 7 | 2023 |
| A general infrastructure for data-driven control design and implementation in tokamaks J Abbate, R Conlin, R Shousha, K Erickson, E Kolemen Journal of Plasma Physics 89 (1), 895890102, 2023 | 7 | 2023 |
| Avoiding tokamak tearing instability with artificial intelligence E Kolemen, J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, K Erickson, ... | 3 | 2023 |
| Highest fusion performance without harmful edge energy bursts in tokamak SK Kim, R Shousha, SM Yang, Q Hu, SH Hahn, A Jalalvand, JK Park, ... Nature communications 15 (1), 3990, 2024 | 2 | 2024 |
| Improved real-time equilibrium reconstruction with kinetic constraints on DIII-D and NSTX-U R Shousha, J Ferron, Z Xing, A Nelson, K Erickson, E Kolemen APS Division of Plasma Physics Meeting Abstracts 2022, PP11. 011, 2022 | 2 | 2022 |
| Safe and efficient access to ELM suppression in KSTAR using low-n edge localized RMP S Yang, JK Park, N Logan, YM Jeon, Q Hu, S Kim, WH Ko, G Park, J Lee, ... APS Division of Plasma Physics Meeting Abstracts 2022, JO03. 006, 2022 | 2 | 2022 |
| Overview of the KSTAR experiments toward fusion reactor WH Ko, SW Yoon, WC Kim, JG Kwak, K Park, YU Nam, S Wang, J Chung, ... Nuclear Fusion, 2024 | 1 | 2024 |
| Implementation of a IMM Kalman Filter-based real-time event detector on the TCV tokamak R Shousha PhD thesis (Eindhoven University of Technology, Eindhoven, Mar. 2018). https …, 2022 | 1 | 2022 |
| Achieving ELM-suppressed Operation with the Highest Performance in DIII-D and KSTAR via Adaptive and Machine Learning Controls SK Kim, R Shousha, SM Yang, Q Hu, S Hahn, A Jalavand, JK Park, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Design of a PID controller for the pedestal top electron density at KSTAR M Kim, SK Kim, K Erickson, A Rothstein, R Shousha, SH Han, J Juhn, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Development of Real-time Feedback Control System for LTX-ꞵ R Shousha, K Erickson, D Boyle, A Maan, C Hansen, S Banerjee, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Calculating ideal β-limit and testing feasibility of adding a second neutral beam with M3D-C1 in LTX-β S Banerjee, D Boyle, A Maan, N Ferraro, R Majeski, R Shousha, S Kubota, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| RTCAKENN: Predicting 7 kinetic profiles in real-time on DIII-D with enhanced robustness to diagnostic limitations and approaching offline level accuracy for FPP advancement R Shousha, J Seo, K Erickson, Z Xing, SK Kim, J Abbate, E Kolemen Bulletin of the American Physical Society, 2024 | | 2024 |
Egemen KolemenPrinceton University在 princeton.edu 的电子邮件经过验证
