The research objective of this award is to develop a Design for Calibration (DfC) methodology for enhancing identifiability in simulation models. Identifiability refers to the difficulty in separating two general sources of uncertainty - parameter uncertainty and model uncertainty - in predictive modeling. Parameter uncertainty results from imperfect knowledge of the underlying physical parameters, and model uncertainty results from approximations and other inaccuracies in a simulation model. To ensure proper identifiability of these uncertainties when combining abundant simulation data with limited physical experimental data, the research takes advantage of two key enabling factors, the first being the inherently multi-response nature of computer simulations, and the second being the availability of extensive simulation results prior to designing the physical experiment. Using spatial random field modeling within a Bayesian framework, the methodology will determine the best subset of response variables to measure experimentally and the most efficient combination of input settings to use over the experiments, with the objective of optimally enhancing identifiability of the uncertainties.
If successful, the results of this research will help ensure identifiability of predictive uncertainties in a manner that allows limited experimental resources to be used most efficiently. This is critically important in simulation-based engineering and science across all engineering disciplines for many reasons that extend beyond achieving good myopic prediction. Learning and distinguishing the true physical parameters and simulation model inaccuracies has broad-reaching implications for i) new product/process designs that are much more complex than the experimental testbed, ii) improving future generations of simulation code, and iii) providing more accurate prediction over a wider set of input regions. Because the methodology is not tied to a particular type of simulation code or application domain, it is expected to be widely applicable. This work will leverage the broad-based constituency of the interdisciplinary doctoral Predictive Science and Engineering Design cluster at Northwestern, through which multidisciplinary testbed applications will be drawn and the results disseminated throughout different engineering domains.
