an:05682913
Zbl 1185.62142
Loeppky, Jason L.; Moore, Leslie M.; Williams, Brian J.
Batch sequential designs for computer experiments
EN
J. Stat. Plann. Inference 140, No. 6, 1452-1464 (2010).
00259145
2010
j
62L05 68U20 05B15 65C60
computer experiment; Gaussian process; random function; Latin hypercube sample; maximin distance; entropy
Summary: Computer models simulating a physical process are used in many areas of science. Due to the complex nature of these codes it is often necessary to approximate the code, which is typically done using a Gaussian process. In many situations the number of code runs available to build the Gaussian process approximation is limited. When the initial design is small or the underlying response surface is complicated this can lead to poor approximations of the code output. In order to improve the fit of the model, sequential design strategies must be employed. We introduce two simple distance based metrics that can be used to augment an initial design in a batch sequential manner. In addition we propose a sequential updating strategy to an orthogonal array based Latin hypercube sample. We show via various real and simulated examples that the distance metrics and the extension of the orthogonal array based Latin hypercubes work well in practice.