Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session F24: Matter at Extreme Conditions: Simulations
8:00 AM–10:48 AM,
Tuesday, March 15, 2022
Room: McCormick Place W-186C
Sponsoring
Unit:
GSCCM
Chair: Kien Nguyen Cong, University of South Florida
Abstract: F24.00006 : Uncertainty Propagation in Errors-in-Variables Equation-of-State Model*
9:24 AM–9:36 AM
Presenter:
Lin H Yang
(Lawrence Livermore Natl Lab)
Authors:
Lin H Yang
(Lawrence Livermore Natl Lab)
Jim A Gaffney
(Lawrence Livermore Natl Lab)
Suzanne J Ali
(Lawrence Livermore Natl Lab)
Amy E Jenei
(Lawrence Livermore National Laboratory)
Gaussian Processes (GPs) are a class of kernel methods that can provide a potential alternative to the current approach that does not contain data uncertainty in the model. GPs come equipped with the possibility to obtain a predictive variance (i.e. error bars, confidence intervals) for each prediction. Unfortunately, the GP formulation usually assumes that there is no noise in the inputs, only in the observations. In this work we will formulate an Errors-in-Variables GP EOS model that explores the processes of propagating both input and output uncertainties to model uncertainty in EOS table generation. We will demonstrate the approach using experimental and simulation data for Boron Carbide and explore the robustness of uncertainty propagation in the resulting EOS due to data sparsity, experiment and simulation noises and extrapolation away from available data.
*This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.
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