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Julian Kates-Harbeck
Julian Kates-Harbeck
Verified email at g.harvard.edu
Title
Cited by
Cited by
Year
Predicting disruptive instabilities in controlled fusion plasmas through deep learning
J Kates-Harbeck, A Svyatkovskiy, W Tang
Nature 568 (7753), 526-531, 2019
1402019
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
162017
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
162013
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
162013
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
112016
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
82021
Magnetic Nuclear Fusion
J Kates-Harbeck
Physics, 0
4
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
32022
Accelerating progress towards controlled fusion power via deep learning at the largest scale
J KATES-HARBECK
in Nature, 2019
12019
Tackling complexity and nonlinearity in plasmas and networks using artificial intelligence and analytical methods
J Kates-Harbeck
Harvard University, 2019
12019
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
12018
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
12018
A two-stage citation recommendation system
J Kates-Harbeck, M Haggblade
Stanford University, 2013
12013
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
12011
Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System
W Tang, G Dong, J Barr, K Erickson, R Conlin, MD Boyer, ...
arXiv preprint arXiv:2204.01289, 2022
2022
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
Simplex-In-Cell Method for Kinetic Plasma Simulation
S Totorica, J Kates-Harbeck, J Zrake, T Abel
APS Division of Plasma Physics Meeting Abstracts 2014, YP8. 031, 2014
2014
The eHealth service CANKADO to overcome nonadherence during oral self-medication.
J Kates-Harbeck, R Wuerstlein, RE Kates, N Harbeck, T Schinkothe
Journal of Clinical Oncology 32 (15_suppl), e17520-e17520, 2014
2014
Computational and Experimental Study of Electromagnetic Wave Heating in Magnetized Plasmas
J Kates-Harbeck
Stanford University, 2013
2013
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