Deep learning for NLTE spectral opacities

Machine Learning design of Volume of Fluid schemes for compressible flows

(2020) Bruno Després et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Towards calibration-invariant spectroscopy using deep learning

(2019) M. Chatzidakis et al.   Scientific Reports

Making inertial confinement fusion models more predictive

(2019) Jim A. Gaffney et al.   PHYSICS OF PLASMAS

Variance reduction method for particle transport equation in spherical geometry

(2018) X. Blanc et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Development and validation of the TROLL radiation-hydrodynamics code for 3D hohlraum calculations

(2018) Erik Lefebvre et al.   NUCLEAR FUSION

Deep Neural Network Initialization With Decision Trees

(2018) Kelli D. Humbird et al.   IEEE Transactions on Neural Networks and Learning Systems

Progress towards a more predictive model for hohlraum radiation drive and symmetry

(2017) O. S. Jones et al.   PHYSICS OF PLASMAS

Zonal flow generation in inertial confinement fusion implosions

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

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

The fast non-LTE code DEDALE

(2015) Franck Gilleron et al.   High Energy Density Physics

Detailed implosion modeling of deuterium-tritium layered experiments on the National Ignition Facility

(2013) D. S. Clark et al.   PHYSICS OF PLASMAS

The role of a detailed configuration accounting (DCA) atomic physics package in explaining the energy balance in ignition-scale hohlraums

(2011) M.D. Rosen et al.   High Energy Density Physics

Advances in NLTE modeling for integrated simulations

(2009) H.A. Scott et al.   High Energy Density Physics