Improving High-Dimensional Physics Models Through Bayesian Calibration With Uncertain Data

We address the problem of calibrating model parameters in computational models to match uncertain and limited experimental data using a Bayesian framework. We employ a modified version of the Bayesian calibration framework proposed by Kennedy and O’Hagan [15], to perform calibration of large dimensional industrial problems. Results for two nonlinear industrial problems with 15 and 100 calibration parameters are presented. The unique advantages of the Bayesian framework are presented along with a discussion on the challenges in calibrating large number of parameters with uncertain and limited data.Copyright © 2012 by ASME