Presented at Los Alamos Computer Science Institute Workshop on Simulation-Driven Optimization, October 27, 2003, Santa Fe

Inference about simulation-code models from experimental data

Kenneth M. Hanson
Los Alamos National Laboratory


I present an approach for making inferences about physics submodels incorporated in a simulation code. Through a Bayesian analysis of a hierarchy of experiments that explore various aspects of the physics submodels, one can determine parameters and their uncertainties. As an example of this approach, I describe an effort to describe the plastic-flow characteristics of a high-strength steel by combining data from basic material characterization tests with an analysis of Taylor impact experiments. A thorough analysis of the material-characterization experiments is described, which necessarily includes the systematic uncertainties that arise form sample-to sample variations in the plastic behavior of the specimens. The Taylor experiments can only be understood by means of a simulation code. I describe how this analysis can be done and how the results can be combined with the results of analyses of data from simpler material-characterization experiments.

Keywords: simulation uncertainty, uncertainty quantification, systematic uncertainty, Bayesian analysis, Taylor impact test, material characterization experiments, Zerilli-Armstrong model, material model, HSLA 100 steel

Viewgraphs for this paper (pdf, 671 KB)
Send e-mail to author at

Return to Hanson presentations