Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling
- National Cheng Kung Univ. (Taiwan)
- National Taiwan Univ. (Taiwan)
- Georgia Inst. of Technology, Atlanta, GA (United States)
A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction of sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.
- Research Organization:
- Georgia Institute of Technology, Atlanta, GA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- SC0010548
- OSTI ID:
- 1405180
- Report Number(s):
- DOE-GT-0010548-6; FG02-13ER26159
- Journal Information:
- Technometrics, Vol. 59, Issue 2; ISSN 0040-1706
- Publisher:
- Taylor & FrancisCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Integrated multiresponse parameter and tolerance design with model parameter uncertainty
|
journal | November 2019 |
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