Stochastic Data Assimilation with Application to Multi-Phase Flow and Health Monitoring Problems

Model-based predictions are critically dependent on assumptions and hypotheses that are not based on first principles and that cannot necessarily be justified based on known prevalent physics. Constitutive models, for instance, fall under this category. While these predictive tools are typically calibrated using observational data, little is usually done with the scatter in the thus-calibrated model parameters. In this study, this scatter is used to characterize the parameters as stochastic processes and a procedure is developed to carry out model validation for ascertaining the confidence in the predictions from the model.