Bayesian methodology for latent function modeling in applied physics and engineering

Computer simulators play a key role in modern science and engineering as a tool for understanding and exploring physical systems. Calibration and validation are important parts of the use of simulators. Calibration is a necessary part of assessing the predictive capability of the model with fully quantified sources of uncertainty. Field observations for physical systems often have diverse types. New methodology for calibration with generalized measurement error structure is proposed and applied to the parallel deterministic transport model for the Center for Exascale Radiation Transport at Texas A\&M University. Validation of computer models is critical for building trust in a simulator. We propose a new methodology for model validation using goodness-of-fit hypothesis tests in a Bayesian model assessment framework. Lastly, the use of a hidden Markov model with a particle filter is proposed for detection of anomalies in time series for the purpose of identifying intrusions in cyber-physical networks.