Gradient-Based Criteria For Sequential Experiment Design

Computer experiments are often used as inexpensive alternatives to real-world experiments. Statistical metamodels of the computer model's input-output behavior can be constructed to serve as approximations of the response surface of the real-world system. The suitability of a metamodel depends in part on its intended use. While decision makers may want to understand the entire response surface, they may be particularly keen on finding interesting regions of the design space, such as where the gradient is steep. We present an adaptive, value-enhanced batch sequential algorithm that samples more heavily in such areas while still providing an understanding of the entire surface. The design points within each batch can be run in parallel to leverage modern multi-core computing assets. We illustrate our approach for deterministic computer models, but it has potential for stochastic simulation models as well.