Latent variable Gaussian process models: A rank‐based analysis and an alternative approach-Reference-Cited by-同舟云学术

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Latent variable Gaussian process models: A rank‐based analysis and an alternative approach

Published:2021-04-28 Issue:15 Volume:122 Page:4007-4026
ISSN:0029-5981
Container-title:International Journal for Numerical Methods in Engineering
language:en
Short-container-title:Int J Numer Methods Eng

Author:

Tao Siyu¹^ORCID,Apley Daniel W.²,Plumlee Matthew²,Chen Wei¹^ORCID

Affiliation:

1. Department of Mechanical Engineering Northwestern University Evanston Illinois USA

2. Department of Industrial Engineering and Management Sciences Northwestern University Evanston Illinois USA

Funder

Division of Civil, Mechanical and Manufacturing Innovation

Small Business Innovative Research and Small Business Technology Transfer

National Science Foundation

Publisher

Wiley

Subject

Applied Mathematics,General Engineering,Numerical Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.6690

Reference35 articles.

1. Design and Analysis of Computer Experiments

2. Predicting the output from a complex computer code when fast approximations are available

3. Leveraging the nugget parameter for efficient Gaussian process modeling

4. TaoS ShintaniK BostanabadR et al.Enhanced Gaussian process metamodeling and collaborative optimization for vehicle suspension design optimization. Paper presented at: Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Cleveland Ohio; 2017.

5. Gaussian process emulators for the stochastic finite element method

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1. Nondeterministic Kriging for Probabilistic Systems with Mixed Continuous and Discrete Input Variables;ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering;2024-12

2. GP+: A Python library for kernel-based learning via Gaussian processes;Advances in Engineering Software;2024-09

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