7 citations · 15 across the 7 of their papers we have counts for
13 papers
Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning
Ben Whewell, Ryan G. McClarren
Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurat…
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…
A Realizable Filtered Intrusive Polynomial Moment Method
Graham Alldredge, Martin Frank, Jonas Kusch +1
Intrusive uncertainty quantification methods for hyperbolic problems exhibit spurious oscillations at shocks, which leads to a significant reduction of the overall approximation qu…
Semi-implicit Hybrid Discrete Approximation of Thermal Radiative Transfer
Ryan G. McClarren, James A. Rossmanith, Minwoo Shin
The thermal radiative transfer (TRT) equations form an integro-differential system that describes the propagation and collisional interactions of photons. Computing accurate and ef…
A high-order / low-order (HOLO) algorithm for preserving conservation in time-dependent low-rank transport calculations
Zhuogang Peng, Ryan G. McClarren
Dynamical low-rank (DLR) approximation methods have previously been developed for time-dependent radiation transport problems. One crucial drawback of DLR is that it does not conse…
High-Energy Density Hohlraum Design Using Forward and Inverse Deep Neural Networks
Ryan G. McClarren, I. L. Tregillis, Todd J. Urbatsch +1
We present a study of using machine learning to enhance hohlraum design for opacity measurement experiments. For opacity experiments we desire a hohlraum that, when its interior wa…