Gaussian Process with Input Location Error and Applications to the Composite Parts Assembly Process

• Activity Number: 348 - Applications: Gaussian Process and Computer Experiments
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Wenjia Wang* and Xiaowei Yue and Ben Haaland and C F Jeff Wu
• Companies: SAMSI and Virginia Polytechnic Institute and State University and University of Utah and Georgia Inst of Technology
• Keywords: Gaussian process; Input location error; Stochastic kriging; Composite parts assembly

Abstract

In this work, we investigate Gaussian process regression with input location error, where the inputs are corrupted by noise. Here, we consider the best linear unbiased predictor for two cases, according to whether there is noise at the target untried location or not. We show that the mean squared prediction error does not converge to zero in either case. We investigate the use of stochastic Kriging in the prediction of Gaussian processes with input location error. We show that stochastic Kriging is a good approximation when the sample size is large. Several numerical examples are given to illustrate the results, and a case study on the assembly of composite parts is presented.