LAUSR

Abstract The impact of experimental design choice on the performance of statistical calibration is largely unknown. Calibration is a technique that uses available experimental data to model the relationship between input and response variables to ultimately infer inputs based on newly observed response values. The purpose of this article is to investigate the performance of several experimental designs with regards to inverse prediction via a comprehensive simulation study. Specifically, we compare several design types including traditional response surface designs, algorithmically generated variance optimal designs, and space-filling designs. Results indicate that the choice of design has an impact on calibration performance and provides overall support for the use of I-optimal designs.