| 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 | 48 | 2021 |
| Data-driven profile prediction for DIII-D J Abbate, R Conlin, E Kolemen Nuclear Fusion 61 (4), 046027, 2021 | 40 | 2021 |
| 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 | 22 | 2022 |
| Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks A Jalalvand, AA Kaptanoglu, AV Garcia, AO Nelson, J Abbate, ME Austin, ... Nuclear Fusion 62 (2), 026007, 2021 | 22 | 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 | 22 | 2021 |
| 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 | 19 | 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 | 14 | 2024 |
| 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 | 9 | 2022 |
| 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 | 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 |
| 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 | 4 | 2023 |
| Towards llms as operational copilots for fusion reactors V Mehta, J Abbate, A Wang, A Rothstein, I Char, J Schneider, E Kolemen, ... NeurIPS 2023 AI for Science Workshop, 2023 | 4 | 2023 |
| Automated experimental design of safe rampdowns via probabilistic machine learning V Mehta, J Barr, J Abbate, MD Boyer, I Char, W Neiswanger, E Kolemen, ... Nuclear Fusion 64 (4), 046014, 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 |
| Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks I Char, Y Chung, J Abbate, E Kolemen, J Schneider arXiv preprint arXiv:2404.12416, 2024 | 1 | 2024 |
| 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 |
| Offline Model-Based Reinforcement Learning for Tokamak Control I Char, J Abbate, L Bardóczi, MD Boyer, Y Chung, R Conlin, K Erickson, ... Power (MW) 1500, 2500, 2000 | 1 | 2000 |
| Initial testing of Alfvén Eigenmode feedback control with machine-learning observers on DIII-D A Rothstein, A Jalalvand, J Abbate, K Erickson, E Kolemen Nuclear Fusion, 2024 | | 2024 |
| Large-database cross-verification and validation of tokamak transport models using baselines for comparison J Abbate, E Fable, B Grierson, A Pankin, G Tardini, E Kolemen Physics of Plasmas 31 (4), 2024 | | 2024 |
| AI-based prediction and control of tokamaks: combining simulations and experimental data JA Abbate Princeton University, 2024 | | 2024 |
