Simultaneous inference of equation of state parameters and unknown data errors with uncertainty quantification via hierarchical Bayesian posterior maximization

Journal Article · Tue Oct 21 00:00:00 EDT 2025 · Journal of Applied Physics

DOI:https://doi.org/10.1063/5.0285135· OSTI ID:3003171

Schmid, Abigail Coyle ^[1]; Andrews, Stephen Arthur ^[2]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Colorado, Boulder, CO (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Equations of state (EOSs) are a key component in running hydrodynamic simulations as they relate the thermodynamic states for the material. The Davis reactants EOS is commonly used for modeling high explosives (HEs), and the EOS model parameters are calibrated using material specific data. The calibrations are often performed with uncertainty quantification via Bayesian inference to account for uncertainty in the data and generate ensembles of likely parameters. However, there are relatively few HE data sets to use for calibration and many are historical and lack error information. In this work, we simultaneously calibrate the Davis reactants EOS model parameters and unknown data error terms for the high explosive PBX 9501. To quantify the uncertainty in the models and the data, we use a Bayesian framework for the calibration and compute the hierarchical Bayesian posterior distribution with both a posteriori maximization approach and Markov Chain Monte Carlo. In general, we find that, given our assumptions, the two approaches result in similar calibrated parameters, posterior covariance matrices, and insights about the parameters but that the posterior maximization requires far less computational resources.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: 89233218CNA000001; NA0003962
• OSTI ID: 3003171
• Report Number(s): LA-UR--25-25358; 10.1063/5.0285135; DOPSR-25-T-2136
• Journal Information: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 15 Vol. 138; ISSN 0021-8979; ISSN 1089-7550
• Publisher: American Institute of Physics (AIP)
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Bayesian inference
Bayesian statistics
Covariance and correlation
Equations of state
Explosives
Hugoniot curve
Hydrodynamics simulations
Monte Carlo methods
Thermal effects
Thermodynamic properties
Thermodynamic states and processes
uncertainty quantification