scholarly journals Bayesian nonstationary Gaussian process models via treed process convolutions

2018 ◽  
Vol 13 (3) ◽  
pp. 797-818
Author(s):  
Waley W. J. Liang ◽  
Herbert K. H. Lee
Keyword(s):  
Gaussian Process ◽  
Process Models ◽  
Gaussian Process Models ◽  
Process Convolutions

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

scholarly journals Composite Gaussian process models for emulating expensive functions

2012 ◽  
Vol 6 (4) ◽  
pp. 1838-1860 ◽  
Author(s):  
Shan Ba ◽  
V. Roshan Joseph
Keyword(s):  
Gaussian Process ◽  
Process Models ◽  
Gaussian Process Models

Simulation Optimization for Computer Models With Multivariate Output

2017 ◽  
Author(s):  
Raed Kontar ◽  
Shiyu Zhou ◽  
John Horst
Keyword(s):  
Gaussian Process ◽  
Manufacturing Systems ◽  
Performance Metrics ◽  
Simulation Optimization ◽  
Process Models ◽  
Manufacturing Performance ◽  
Gaussian Process Models ◽  
Multivariate Gaussian ◽  
Potential Applicability ◽  
Multivariate Gaussian Process

This paper explores the potential of Gaussian process based Metamodels for simulation optimization with multivariate outputs. Specifically we focus on Multivariate Gaussian process models established through separable and non-separable covariance structures. We discuss the advantages and drawbacks of each approach and their potential applicability in manufacturing systems. The advantageous features of the Multivariate Gaussian process models are then demonstrated in a case study for the optimization of manufacturing performance metrics.

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