Adaptive combined space-filling and D-optimal designs

摘要

In the beginning of sequential experimentation, space-filling designs are more appropriate for exploring process behaviour since they do not require any assumptions about the underlying model. In the latter stages of sequential experimentation, however, when data are collected and more knowledge about the process behaviour is gathered, model-based optimal designs may be more appropriate. This article proposes an adaptive combined design (ACD) balancing the characteristics of both design criteria at different stages of the sequential experiments. The tuning parameter associated with the ACD adaptively gauges the amount of process knowledge gain, which is used to improve the estimation of model parameters while still allowing for the exploration of model uncertainties. Rather than employing the weighted-sum method, an