Inference regarding complex physical systems (e.g. subsurface aquifers, charged particle accelerators, shock physics) is typically plagued by a lack of information available from relevant, experimental data.  What data is available is usually limited and informs indirectly about the phenomena of interest. However, when the physical system is amenable to computer simulation, these simulations can be combined with experimental observations to give useful information regarding calibration parameters, prediction uncertainty, and model inadequacy.

This talk describes a framework for carrying out such simulation-based predictive investigations which involves experimental design, sensitivity analysis, response surface modeling, parameter estimation and accounting for systematic discrepancies between the simulation output and experimental data.  An application from cosmology will be used as the context for this talk.