  A key objective in engineering problems is to predict an unknown experimental
surface over an input domain. In complex physical experiments, this may be
hampered by response censoring, which results in a significant loss of
information. For such problems, experimental design is paramount for maximizing
predictive power using a small number of expensive experimental runs. To tackle
this, we propose a novel adaptive design method, called the integrated censored
mean-squared error (ICMSE) method. The ICMSE method first estimates the
posterior probability of a new observation being censored, then adaptively
chooses design points that minimize predictive uncertainty under censoring.
Adopting a Gaussian process regression model with product correlation function,
the proposed ICMSE criterion is easy to evaluate, which allows for efficient
design optimization. We demonstrate the effectiveness of the ICMSE design in
two real-world applications on surgical planning and wafer manufacturing.
