Julian Kates-Harbeck
Julian Kates-Harbeck
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Predicting disruptive instabilities in controlled fusion plasmas through deep learning
J Kates-Harbeck, A Svyatkovskiy, W Tang
Nature 568 (7753), 526-531, 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
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
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
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
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
Magnetic Nuclear Fusion
J Kates-Harbeck
Physics, 0
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
Accelerating progress towards controlled fusion power via deep learning at the largest scale
in Nature, 2019
Tackling complexity and nonlinearity in plasmas and networks using artificial intelligence and analytical methods
J Kates-Harbeck
Harvard University, 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
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
A two-stage citation recommendation system
J Kates-Harbeck, M Haggblade
Stanford University, 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
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
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
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
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
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
Computational and Experimental Study of Electromagnetic Wave Heating in Magnetized Plasmas
J Kates-Harbeck
Stanford University, 2013
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