A Tutorial on Bayesian Analysis of Linear Shock Compression Data

TL;DR

This paper introduces a Bayesian approach to analyze shock compression data, enabling the generation of multiple consistent Hugoniot curves with quantified uncertainty, improving upon traditional least squares methods.

Contribution

It presents a Bayesian framework for propagating measurement uncertainty to model parameters and predictions, providing a more flexible and interpretable analysis of shock compression data.

Findings

Bayesian method produces multiple consistent Hugoniot curves.

The approach is computationally inexpensive and less sensitive to outliers.

Demonstrated with data on argon, copper, and nickel.

Abstract

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure-volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this tutorial shows how to sample multiple Hugoniot curves in the pressure-volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine-Hugoniot equations to yield Hugoniot curves in the pressure-volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and made all code and data available at https://github.com/llnl/BALSCD.