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Deep learning for NLTE spectral opacities

https://doi.org/10.1063/5.0006784
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/advs.201801367openalex-snapshot
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
inferredcites →https://doi.org/10.1016/j.hedp.2009.07.003openalex-snapshot
Advances in NLTE modeling for integrated simulations
inferredcites →https://doi.org/10.1016/j.hedp.2011.03.008openalex-snapshot
The role of a detailed configuration accounting (DCA) atomic physics package in explaining the energy balance in ignition-scale hohlraums
inferredcites →https://doi.org/10.1016/j.hedp.2015.07.001openalex-snapshot
The fast non-LTE code DEDALE
inferredcites →https://doi.org/10.1016/j.jcp.2018.02.015openalex-snapshot
Variance reduction method for particle transport equation in spherical geometry
inferredcites →https://doi.org/10.1016/j.jcp.2020.109275openalex-snapshot
Machine Learning design of Volume of Fluid schemes for compressible flows
inferredcites →https://doi.org/10.1016/s0022-4073(01)00109-1openalex-snapshot
Cretin—a radiative transfer capability for laboratory plasmas
inferredcites →https://doi.org/10.1017/jfm.2016.615openalex-snapshot
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
inferredcites →https://doi.org/10.1038/s41467-017-00839-3openalex-snapshot
Bypassing the Kohn-Sham equations with machine learning
inferredcites →https://doi.org/10.1038/s41598-019-38482-1openalex-snapshot
Towards calibration-invariant spectroscopy using deep learning
inferredcites →https://doi.org/10.1063/1.1143558openalex-snapshot
Artificial neural networks for plasma x-ray spectroscopic analysis
inferredcites →https://doi.org/10.1063/1.1885003openalex-snapshot
Increasing robustness of indirect drive capsule designs against short wavelength hydrodynamic instabilities
inferredcites →https://doi.org/10.1063/1.4802194openalex-snapshot
Detailed implosion modeling of deuterium-tritium layered experiments on the National Ignition Facility
inferredcites →https://doi.org/10.1063/1.4977912openalex-snapshot
Zonal flow generation in inertial confinement fusion implosions
inferredcites →https://doi.org/10.1063/1.4982693openalex-snapshot
Progress towards a more predictive model for hohlraum radiation drive and symmetry
inferredcites →https://doi.org/10.1063/1.5108667openalex-snapshot
Making inertial confinement fusion models more predictive
inferredcites →https://doi.org/10.1063/1.872004openalex-snapshot
Three-dimensional simulations of Nova high growth factor capsule implosion experiments
inferredcites →https://doi.org/10.1088/1741-4326/aacc9copenalex-snapshot
Development and validation of the TROLL radiation-hydrodynamics code for 3D hohlraum calculations
inferredcites →https://doi.org/10.1088/2632-2153/ac3ffaopenalex-snapshot
Building high accuracy emulators for scientific simulations with deep neural architecture search
inferredcites →https://doi.org/10.1098/rspa.1962.0101openalex-snapshot
Recombination between electrons and atomic ions, I. Optically thin plasmas
inferredcites →https://doi.org/10.1103/physreve.99.053207openalex-snapshot
Interpretation of proton radiography experiments of hohlraums with three-dimensional simulations
inferredcites →https://doi.org/10.1109/tnnls.2018.2869694openalex-snapshot
Deep Neural Network Initialization With Decision Trees
inferredcites →https://doi.org/10.13140/rg.2.2.34431.46245openalex-snapshot
Machine learning approach for the solution of the Riemann problem in fluid dynamics

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deterministic Neural Networks Optimization from a Continuous and Energy Point of View
inferred← citeshttps://doi.org/10.1016/j.anucene.2022.109276openalex-snapshot
Data reduction in deterministic neutron transport calculations using machine learning
inferred← citeshttps://doi.org/10.1016/j.hedp.2022.100998openalex-snapshot
The bands method for tabulating NLTE material properties
inferred← citeshttps://doi.org/10.1016/j.hedp.2025.101199openalex-snapshot
Deep learning surrogate models to solve time-dependent NLTE absorption and emission spectra
inferred← citeshttps://doi.org/10.1016/j.jcp.2026.114707openalex-snapshot
Neural network sampling of Bethe-Heitler process in particle-in-cell codes
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inferred← citeshttps://doi.org/10.1017/hpl.2023.47openalex-snapshot
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inferred← citeshttps://doi.org/10.1017/s0022377822001246openalex-snapshot
Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations
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Machine learning on the ignition threshold for inertial confinement fusion
inferred← citeshttps://doi.org/10.1063/5.0097777openalex-snapshot
Enhanced analysis of experimental x-ray spectra through deep learning
inferred← citeshttps://doi.org/10.1063/5.0102624openalex-snapshot
Using tabulated NLTE data for Hohlraum simulations
inferred← citeshttps://doi.org/10.1063/5.0126708openalex-snapshot
Neural network surrogate models for equations of state
inferred← citeshttps://doi.org/10.1063/5.0128661openalex-snapshot
Advanced data analysis in inertial confinement fusion and high energy density physics
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A method for calculating the radiation characteristics of nonequilibrium laboratory plasmas
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inferred← citeshttps://doi.org/10.1063/5.0261956openalex-snapshot
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inferred← citeshttps://doi.org/10.1088/1361-6463/adb58fopenalex-snapshot
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inferred← citeshttps://doi.org/10.1103/5wvx-p626openalex-snapshot
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inferred← citeshttps://doi.org/10.1103/physrevlett.134.185101openalex-snapshot
Reduced Model of Ionization Lag in Intense Laser-Produced Plasmas
inferred← citeshttps://doi.org/10.1103/physrevresearch.7.023150openalex-snapshot
Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations
inferred← citeshttps://doi.org/10.1109/mlhpc54614.2021.00014openalex-snapshot
Is Disaggregation possible for HPC Cognitive Simulation?
inferred← citeshttps://doi.org/10.1109/tps.2022.3227506openalex-snapshot
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models
inferred← citeshttps://doi.org/10.1140/epjs/s11734-026-02260-8openalex-snapshot
A residual neural network surrogate model for fast NLTE spectral calculations in gold plasmas
inferred← citeshttps://doi.org/10.2139/ssrn.4241669openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.6996785openalex-snapshot
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inferred← citeshttps://doi.org/10.23919/at-ap-rasc54737.2022.9814235openalex-snapshot
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