Parameter Screening in Dynamic Computer Model Calibration Using Global Sensitivities

Sensitivity analysis and computer model calibration are generally treated as two separate topics. In sensitivity analysis one quantifies the effect of each input factor on outputs, whereas in calibration one finds the values of input factors that provide the best match to a set of field data. In this paper we show a connection between these two seemingly separate concepts, and illustrate it with an automotive industry application involving a Road Load Acquisition Data (RLDA) computer model. We use global sensitivity analysis for computer models with transient responses to screen out inactive input parameters and make the calibration algorithm numerically more stable. Because the computer model can be computationally intensive, we construct a fast statistical surrogate for the computer model with transient responses. This fast surrogate is used for both sensitivity analysis and RLDA computer model calibration.