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Title: Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function

Abstract

This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability (MAP) estimate of a function subject to constraints (e. g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While the present work focuses on a model of the Equation Of State (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semi-parametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference, and identifies how each experiment most constrains the result.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC). Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1544720
Report Number(s):
[LA-UR-18-27132]
[Journal ID: ISSN 2377-2158]
Grant/Contract Number:  
[89233218CNA000001]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Verification, Validation and Uncertainty Quantification
Additional Journal Information:
[ Journal Volume: 4; Journal Issue: 1]; Journal ID: ISSN 2377-2158
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Uncertainty Quantification; semi-parametric; Bayesian; Equation of State; High Explosives

Citation Formats

Andrews, Stephen Arthur, and Fraser, Andrew Mcleod. Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function. United States: N. p., 2019. Web. doi:10.1115/1.4043807.
Andrews, Stephen Arthur, & Fraser, Andrew Mcleod. Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function. United States. doi:10.1115/1.4043807.
Andrews, Stephen Arthur, and Fraser, Andrew Mcleod. Tue . "Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function". United States. doi:10.1115/1.4043807.
@article{osti_1544720,
title = {Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function},
author = {Andrews, Stephen Arthur and Fraser, Andrew Mcleod},
abstractNote = {This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability (MAP) estimate of a function subject to constraints (e. g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While the present work focuses on a model of the Equation Of State (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semi-parametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference, and identifies how each experiment most constrains the result.},
doi = {10.1115/1.4043807},
journal = {Journal of Verification, Validation and Uncertainty Quantification},
number = [1],
volume = [4],
place = {United States},
year = {2019},
month = {6}
}

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