Julian Kates-Harbeck

发表

Simplex-in-cell technique for collisionless plasma simulations

Julian Kates-Harbeck, Samuel Totorica, Jonathan Zrake, 2015, J. Comput. Phys..

FULLY CONVOLUTIONAL SPATIO-TEMPORAL MODELS FOR REPRESENTATION LEARNING IN PLASMA SCIENCE

Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang, 2020, 2007.10468.

Quantifying and Propagating Uncertainties to Enhance Real-time Disruption Prediction with Machine Learning

Todd A. Oliver, Craig Michoski, D. R. Hatch, 2018 .

Tokamak Disruption Predictions Based on Deep Learning Temporal Convolutional Neural Networks

Kyle G. Felker, Alexey Svyatkovskiy, Julian Kates-Harbeck, 2020 .

Tackling complexity and nonlinearity in plasmas and networks using artificial intelligence and analytical methods

Julian Kates-Harbeck, J. Kates-Harbeck, 2019 .

Highlights from the community white paper “Enhancing US fusion science with data-centric technologies”

David R. Smith, Cristina Rea, Robert S. Granetz, 2018 .

Galaxy Image Processing and Morphological Classification Using Machine Learning

Julian Kates-Harbeck, 2012 .

Training distributed deep recurrent neural networks with mixed precision on GPU clusters

Julian Kates-Harbeck, William Tang, Alexey Svyatkovskiy, 2017, MLHPC@SC.

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Julian Kates-Harbeck, William Tang, Alexey Svyatkovskiy, 2019, Nature.

Kernel Flux: a whole-head 432-magnetometer optically-pumped magnetoencephalography (OP-MEG) system for brain activity imaging during natural human experiences

Hooman Mohseni, Benjamin Shapiro, Julian Kates-Harbeck, 2021, OPTO.

Kernel flow: a high channel count scalable TD-fNIRS system

Hamid Dehghani, Viswanath Gopalakrishnan, Julian Kates-Harbeck, 2021, BiOS.