| Predicting disruptive instabilities in controlled fusion plasmas through deep learning J Kates-Harbeck, A Svyatkovskiy, W Tang Nature 568 (7753), 526-531, 2019 | 290 | 2019 |
| Training distributed deep recurrent neural networks with mixed precision on GPU clusters A Svyatkovskiy, J Kates-Harbeck, W Tang Proceedings of the Machine Learning on HPC Environments, 1-8, 2017 | 28 | 2017 |
| 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 | 19 | 2022 |
| Simplified biased random walk model for RecA-protein-mediated homology recognition offers rapid and accurate self-assembly of long linear arrays of binding sites J Kates-Harbeck, A Tilloy, M Prentiss Physical Review E 88 (1), 012702, 2013 | 18 | 2013 |
| Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange J Vlassakis, E Feinstein, D Yang, A Tilloy, D Weiller, J Kates-Harbeck, ... Physical Review E 87 (3), 032702, 2013 | 18 | 2013 |
| Fully convolutional spatio-temporal models for representation learning in plasma science G Dong, KG Felker, A Svyatkovskiy, W Tang, J Kates-Harbeck Journal of Machine Learning for Modeling and Computing 2 (1), 2021 | 12 | 2021 |
| Evolutionary instability of selfish learning in repeated games A McAvoy, J Kates-Harbeck, K Chatterjee, C Hilbe PNAS nexus 1 (4), pgac141, 2022 | 11 | 2022 |
| Simplex-in-cell technique for collisionless plasma simulations J Kates-Harbeck, S Totorica, J Zrake, T Abel Journal of Computational Physics 304, 231-251, 2016 | 11 | 2016 |
| Magnetic Nuclear Fusion J Kates-Harbeck Physics, 0 | 4 | |
| Accelerating progress towards controlled fusion power via deep learning at the largest scale J KATES-HARBECK in Nature, 2019 | 1 | 2019 |
| Tackling complexity and nonlinearity in plasmas and networks using artificial intelligence and analytical methods J Kates-Harbeck Harvard University, 2019 | 1 | 2019 |
| Highlights from the community white paper``Enhancing US fusion science with data-centric technologies'' D Smith, R Granetz, M Greenwald, J Kates-Harbeck, E Kolemen, ... APS Division of Plasma Physics Meeting Abstracts 2018, NP11. 132, 2018 | 1 | 2018 |
| Quantifying and propagating uncertainties to enhance real-time disruption prediction with machine learning C Michoski, J Kates-Harbeck, G Merlo, M Bremer, A Shukla, N Logan, ... APS Division of Plasma Physics Meeting Abstracts 2018, CM10. 002, 2018 | 1 | 2018 |
| A two-stage citation recommendation system J Kates-Harbeck, M Haggblade Stanford University. Kates-Harbeck J. Haggblade M, 2013 | 1 | 2013 |
| Fractional Resonances in Ion Bernstein Wave Heating in a Helicon Plasma Discharge J Kates-Harbeck APS Division of Plasma Physics Meeting Abstracts 53, JP9. 081, 2011 | 1 | 2011 |
| Trust based attachment J Kates-Harbeck, M Nowak Plos one 18 (8), e0288142, 2023 | | 2023 |
| Social network structure and the spread of complex contagions from a population genetics perspective J Kates-Harbeck, MM Desai Physical Review E 108 (2), 024306, 2023 | | 2023 |
| 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 |
| Tokamak Disruption Predictions Based on Deep Learning Temporal Convolutional Neural Networks G Dong, K Felker, A Svyatkovskiy, W Tang, J Kates-Harbeck APS Division of Plasma Physics Meeting Abstracts 2020, BO05. 002, 2020 | | 2020 |
| Deep Learning Studies Linking Tokamak Disruption to Neoclassical Tearing Modes (NTM's) G Dong, J Kates-Harbeck, N McGreivy, Z Lin, W Tang APS Division of Plasma Physics Meeting Abstracts 2019, PP10. 106, 2019 | | 2019 |
Antoine TilloyMines Paris - PSLVerified email at mines-paristech.fr
