Multi-Objective Design Optimization for Computer Experiments

• Activity Number: 627
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Quality and Productivity Section
• Author(s): Werner Mueller*+
• Companies: Johannes-Kepler-University Linz
• Keywords: optimal design ; random field ; pareto front

Abstract

For estimation and predictions of random fields it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging are typically non-space-filling and very costly to determine. In this paper, we investigate the possibility of using criteria inspired by an equivalence theorem type relation to build designs quasi-optimal for the empirical kriging variance. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, while the second uses the surrogate criterion as local heuristic to chose the points at which the (costly) true Empirical Kriging variance is effectively computed. We illustrate the performance of the algorithms presented to both a simple simulated example and to a real oceanographic dataset.