114 citations · 288 across the 10 of their papers we have counts for
10 papers
General Relativistic Implicit Monte Carlo Radiation-Hydrodynamics
Nathaniel Roth, Peter Anninos, Peter B. Robinson +4
We report on a new capability added to our general relativistic radiation-magnetohydrodynamics code, Cosmos++: an implicit Monte Carlo (IMC) treatment for radiation transport. The…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
Transfer learning driven design optimization for inertial confinement fusion
K. D. Humbird, J. L. Peterson
Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural netwo…
The data-driven future of high energy density physics
Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson +20
The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fu…
Experiments conducted in the burning plasma regime with inertial fusion implosions
J. S. Ross, J. E. Ralph, A. B. Zylstra +149
An experimental program is currently underway at the National Ignition Facility (NIF) to compress deuterium and tritium (DT) fuel to densities and temperatures sufficient to achiev…
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data
K. D. Humbird, J. L. Peterson, J. Salmonson +1
The design space for inertial confinement fusion (ICF) experiments is vast and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the…
Transfer learning to model inertial confinement fusion experiments
K. D. Humbird, J. L. Peterson, R. G. McClarren
Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict exp…
Predicting the time-evolution of multi-physics systems with sequence-to-sequence models
K. D. Humbird, J. L. Peterson, R. G. McClarren
In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer si…
Deep neural network initialization with decision trees
K. D. Humbird, J. L. Peterson, R. G. McClarren
In this work a novel, automated process for constructing and initializing deep feed-forward neural networks based on decision trees is presented. The proposed algorithm maps a coll…
An Enhanced Nonlinear Critical Gradient for Electron Turbulent Transport due to Reversed Magnetic Shear
J. L. Peterson, G. W. Hammett, D. R. Mikkelsen +5
The first nonlinear gyrokinetic simulations of electron internal transport barriers (e-ITBs) in the National Spherical Torus Experiment show that reversed magnetic shear can suppre…