Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer [Slides]

Technical Report · Thu May 07 00:00:00 EDT 2020

DOI:https://doi.org/10.2172/1618308· OSTI ID:1618308

Matsekh, Anna M.  ^[1]; Chacon, Luis  ^[1]; Park, HyeongKae  ^[1]; Chen, Guangye  ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Project Goal: Use parametric Machine Learning methods in order to reduce memory requirements at checkpointing & restarting in the IMC simulations of Thermal Radiative Transfer using: Expectation Maximization and Weighted Gaussian Mixture Model-based approach for `particle-data compression', introduced in Plasma Physics to model Maxwellian particle distributions by Luis Chacon and Guangye Chen; Expectation Maximization with Weighted Hyper-Erlang Model in order to compress isotropic IMC particle data in the frequency domain; and Expectation Maximization and von Mises-Fisher Mixture Model for compression of anisotropic IMC particle data in the angular domain (work-in-progress).

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: 89233218CNA000001
• OSTI ID: 1618308
• Report Number(s): LA-UR-20-23438; TRN: US2106591
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
73 NUCLEAR PHYSICS AND RADIATION PHYSICS
expectation maximization
thermal radiative transfer
Implicit Monte Carlo