Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors

Download

PDF - Pre print Restricted to Registered users only Download (178Kb) | Request a copy

Description/Abstract

The selection of stationary or non-stationary
Kriging to create a surrogate model of a black box
function requires apriori knowledge of the nature of
response of the function as these techniques are bet-
ter at representing some types of responses than oth-
ers. While an adaptive technique has been previously
proposed to adjust the level of stationarity within the
surrogate model such a model can be prohibitively ex-
pensive to construct for high dimensional problems. An
alternative approach is to employ a surrogate model
constructed from an ensemble of stationary and non-
stationary Kriging models. The following paper assesses
the accuracy and optimization performance of such a
modelling strategy using a number of analytical func-
tions and engineering design problems.