Analysis of Computer Experiments with Functional Response

• Activity Number: 307
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Ying Hung*+
• Companies: Rutgers University
• Keywords: EM Algorithm ; Gaussian Process Model ; Gibbs Sampling ; Kriging ; Latin Hypercube Design ; Optimization

Abstract

This study is motivated by a computer experiment conducted for optimizing residual stresses in the machining of metals. Although kriging is widely used in the analysis of computer experiments, it cannot be easily applied to model the residual stresses because they are obtained as a profile. The high dimensionality caused by this functional response introduces severe computational challenges in kriging. It is well known that if the functional data are observed on a regular grid, the computations can be simplified using an application of Kronecker products. However, the case of irregular grid is quite complex. In this study, we develop a Gibbs sampling-based expectation maximization algorithm, which converts the irregularly spaced data into a regular grid so that the Kronecker product-based approach can be employed for efficiently fitting a kriging model to the functional data. This is a joint work with Roshan Joseph and Shreyes N. Melkote.