Deep learning: A guide for practitioners in the physical sciences

Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D

(2018) Cristina Rea et al.   FUSION SCIENCE AND TECHNOLOGY

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

A machine learns to predict the stability of circumbinary planets

(2018) Christopher Lam et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Deep learning for biology

(2018) Sarah Webb   NATURE

Simultaneous diagnosis of radial profiles and mix in NIF ignition-scale implosions via X-ray spectroscopy

(2017) O. Ciricosta et al.   PHYSICS OF PLASMAS

Zonal flow generation in inertial confinement fusion implosions

(2017) J. L. Peterson et al.   PHYSICS OF PLASMAS

Ensemble simulations of inertial confinement fusion implosions

(2017) Ryan Nora et al.   Statistical Analysis and Data Mining

Searching for exotic particles in high-energy physics with deep learning

(2014) P. Baldi et al.   Nature Communications

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

(2013) Jesús Vega et al.   FUSION ENGINEERING AND DESIGN