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 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 this 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 semiparametric 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.
Estimating Physics Models and Quantifying Their Uncertainty Using Optimization With a Bayesian Objective Function
Manuscript received July 30, 2018; final manuscript received May 3, 2019; published online June 18, 2019. Assoc. Editor: Tao Xing.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.
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Andrews, S. A., and Fraser, A. M. (June 18, 2019). "Estimating Physics Models and Quantifying Their Uncertainty Using Optimization With a Bayesian Objective Function." ASME. J. Verif. Valid. Uncert. March 2019; 4(1): 011002. https://doi.org/10.1115/1.4043807
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