Global Emulation Through Normative Decision Making and Thrifty Adaptive Batch Sampling

We consider the problem of adaptive sampling for global emulation (metamodeling) with a finite budget. Conventionally this problem is tackled through a greedy sampling strategy, which is optimal for taking either a single sample or a handful of samples at a single sampling stage but neglects the influence of future samples. This raises the question: “Can we optimize the number of sampling stages as well as the number of samples at each stage?”...