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
Citations
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Proceedings ArticleDOI
Aerodynamic Response Quantification of Complex Hypersonic Configurations using Variable Fidelity Surrogate Modeling
TL;DR: This paper seeks to capture the aerodynamic response of complex hypersonic vehicles using variable-fidelity (VF) kriging surrogate models, which merges the response of different flow prediction simulations, varying in flow approximation accuracy, into a single surrogate model.
Journal ArticleDOI
Literature review of bridge structure's optimization and it's development over time
TL;DR: In this article , a review of recent structural optimization research in bridge engineering is presented, which provides a detailed examination of optimization goals and outlines recent research field limitations and provides guidelines for future research proposal in the field of bridge engineering structural optimization.
Journal ArticleDOI
A multi-fidelity active learning method for global design optimization problems with noisy evaluations
TL;DR: In this article , a multi-fidelity active learning method is presented for design optimization problems characterized by noisy evaluations of the performance metrics, which is intended to accurately predict the design performance while reducing the computational effort required by simulation-driven design (SDD) to achieve the global optimum.
Journal ArticleDOI
Modified Multifidelity Surrogate Model Based on Radial Basis Function with Adaptive Scale Factor
TL;DR: In this paper , a modified multifidelity surrogates (MMFS) model based on a radial basis function (RBF) is proposed, in which two fidelities of information can be analyzed by adaptively obtaining the scale factor.
Journal ArticleDOI
ERGO: A New Robust Design Optimization Technique Combining Multi-Objective Bayesian Optimization With Analytical Uncertainty Quantification
TL;DR: The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement (REI), obtaining the novel efficient robust global optimization (ERGO) scheme.
References
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The design and analysis of computer experiments
TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
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Principles of geostatistics
TL;DR: In this article, the authors present a new science leading to such an approach, namely geostatistics, which is a new approach for estimating the estimation of ore grades and reserves.
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A statistical approach to some basic mine valuation problems on the Witwatersrand
TL;DR: In this paper, the application of the lognormal curve to the frequency distribution of gold values is discussed, and some fundamental concepts in application of statistics to mine valuation on the Witwatersrand are discussed.
Journal ArticleDOI
Recent advances in surrogate-based optimization
TL;DR: The present state of the art of constructing surrogate models and their use in optimization strategies is reviewed and extensive use of pictorial examples are made to give guidance as to each method's strengths and weaknesses.
Journal ArticleDOI
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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