Calibration of multi-physics computational models using Bayesian networks

This paper develops a Bayesian network-based method for the calibration of multi-physics models, integrating various sources of uncertainty with information from computational models and experimental data. We adopt the Kennedy and O'Hagan (KOH) framework for model calibration under uncertainty, and develop extensions to multi-physics models and various scenarios of available data. Both aleatoric uncertainty (due to natural variability) and epistemic uncertainty (due to lack of information, inclu...