Journal ArticleDOI
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
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TLDR
It is shown that the gradient-enhanced GHBF proposed in this paper is very promising and can be used to significantly improve the efficiency, accuracy and robustness of VFM in the context of aero-loads prediction.About:
This article is published in Aerospace Science and Technology.The article was published on 2013-03-01. It has received 313 citations till now. The article focuses on the topics: Surrogate model & Kriging.read more
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Locally Optimized Covariance Kriging for Non-Stationary System Responses
Abstract: Clark, Jr., Daniel. M.S.Egr., Department of Mechanical and Materials Engineering, Wright State University, 2016. Locally Optimized Covariance Kriging for Non-Stationary System Responses. In this thesis, the Locally-Optimized Covariance (LOC) Kriging method is developed. This method represents a flexible surrogate modeling approach for approximating a nonstationary Kriging covariance structures for deterministic responses. The non-stationary covariance structure is approximated by aggregating multiple stationary localities. The aforementioned localities are determined to be statistically significant utilizing the NonStationary Identification Test. This methodology is applied to various demonstration problems including simple one and two-dimensional analytical cases, a deterministic fatigue and creep life model, and a five-dimensional fluid-structural interaction problem. The practical significance of LOC-Kriging is discussed in detail and is directly compared to stationary Kriging considering computational cost and accuracy.
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Recent advances in surrogate-based optimization
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A Trust Region Framework for Managing the Use of Approximation Models in Optimization
TL;DR: An analytically robust, globally convergent approach to managing the use of approximation models of varying fidelity in optimization, based on the trust region idea from nonlinear programming, which is shown to be provably convergent to a solution of the original high-fidelity problem.
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