85 citations · 182 across the 12 of their papers we have counts for
12 papers
A joint diffusion approach to multi-modal inference in inertial confinement fusion
Michael S. Jones, Justin Kunimune, Daniel Casey +3
A combination of physics-based simulation and experiments has been critical to achieving ignition in inertial confinement fusion (ICF). Simulation and experiment both produce a mix…
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 as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations
Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird
Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelit…
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…
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models
Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird
Simulations of high energy density physics are expensive, largely in part for the need to produce non-local thermodynamic equilibrium opacities. High-fidelity spectra may reveal ne…
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…
Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements
Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird
Simulations of high energy density physics are expensive in terms of computational resources. In particular, the computation of opacities of plasmas in the non-local thermal equili…
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…
Adjoint-based Sensitivity Analysis for High-Energy Density Radiaitive Transfer using Flux-Limited Diffusion
Kelli D. Humbird, Ryan G. McClarren
Uncertainty quantification and sensitivity analyses are a vital component for predictive modeling in the sciences and engineering. The adjoint approach to sensitivity analysis requ…