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:
-
- 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. https://doi.org/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. https://doi.org/10.1115/1.4043807. https://www.osti.gov/servlets/purl/1544720.
@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 = {Tue Jun 18 00:00:00 EDT 2019},
month = {Tue Jun 18 00:00:00 EDT 2019}
}
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