| Keras2c: A library for converting Keras neural networks to real-time compatible C R Conlin, K Erickson, J Abbate, E Kolemen Engineering Applications of Artificial Intelligence 100, 104182, 2021 | 41 | 2021 |
| Data-driven profile prediction for DIII-D J Abbate, R Conlin, E Kolemen Nuclear Fusion 61 (4), 046027, 2021 | 36 | 2021 |
| Real-time and adaptive reservoir computing with application to profile prediction in fusion plasma A Jalalvand, J Abbate, R Conlin, G Verdoolaege, E Kolemen IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2630-2641, 2021 | 19 | 2021 |
| The DESC stellarator code suite. Part 1. Quick and accurate equilibria computations D Panici, R Conlin, DW Dudt, K Unalmis, E Kolemen Journal of Plasma Physics 89 (3), 955890303, 2023 | 17 | 2023 |
| The DESC stellarator code suite Part 3: Quasi-symmetry optimization DW Dudt, R Conlin, D Panici, E Kolemen Journal of Plasma Physics 89 (2), 955890201, 2023 | 15 | 2023 |
| Offline model-based reinforcement learning for tokamak control I Char, J Abbate, L Bardóczi, M Boyer, Y Chung, R Conlin, K Erickson, ... Learning for Dynamics and Control Conference, 1357-1372, 2023 | 14 | 2023 |
| The DESC stellarator code suite. Part 2. Perturbation and continuation methods R Conlin, DW Dudt, D Panici, E Kolemen Journal of Plasma Physics 89 (3), 955890305, 2023 | 11 | 2023 |
| Greedy permanent magnet optimization AA Kaptanoglu, R Conlin, M Landreman Nuclear Fusion 63 (3), 036016, 2023 | 6 | 2023 |
| 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 | 5 | 2023 |
| Exploration via planning for information about the optimal trajectory V Mehta, I Char, J Abbate, R Conlin, M Boyer, S Ermon, J Schneider, ... Advances in Neural Information Processing Systems 35, 28761-28775, 2022 | 5 | 2022 |
| 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 | 4 | 2023 |
| 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 | 2 | 2024 |
| Avoiding tokamak tearing instability with artificial intelligence E Kolemen, J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, K Erickson, ... | 2 | 2023 |
| Sample-efficient Plasma Control by Planning for Optimal Trajectory Information V Mehta, I Char, J Schneider, W Neiswanger, S Ermon, J Abbate, ... ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the …, 2022 | 1 | 2022 |
| Optimization of nonlinear turbulence in stellarators P Kim, S Buller, R Conlin, W Dorland, DW Dudt, R Gaur, R Jorge, ... Journal of Plasma Physics 90 (2), 905900210, 2024 | | 2024 |
| Magnetic fields with general omnigenity DW Dudt, AG Goodman, R Conlin, D Panici, E Kolemen Journal of Plasma Physics 90 (1), 905900120, 2024 | | 2024 |
| Implementation of AI/DEEP learning disruption predictor into a plasma control system W Tang, G Dong, J Barr, K Erickson, R Conlin, D Boyer, J Kates‐Harbeck, ... Contributions to Plasma Physics 63 (5-6), e202200095, 2023 | | 2023 |
| An ideal MHD δW stability analysis that bypasses the Newcomb equation AS Glasser, AH Glasser, R Conlin, E Kolemen Physics of Plasmas 27 (2), 2020 | | 2020 |
Egemen KolemenPrinceton UniversityVerified email at princeton.edu
