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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Journal ArticleDOI
A hybrid variable-fidelity global approximation modelling method combining tuned radial basis function base and kriging correction
TL;DR: The results show that the proposed hybrid VF can achieve a much more accurate approximation than other metamodelling methods in the small HF sample situation.
Journal ArticleDOI
An overview of gradient-enhanced metamodels with applications
TL;DR: This article is a review of the main metamodels that use function gradients in addition to function values and indicates that there is a trade-off between the better computing time of least squares methods and the larger versatility of kernel-based approaches.
Proceedings ArticleDOI
Advances in Bayesian Optimization with Applications in Aerospace Engineering
TL;DR: Two recent advances in Bayesian optimization are reviewed that tackle the challenges of optimizing expensive functions and thus can enrich the optimization toolbox of the aerospace engineer.
Journal ArticleDOI
An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models
TL;DR: In AL-VFM, Kriging metamodel is adopted to map the difference between the HF and LF models aiming to approach the HF model on the entire domain, and a general active learning strategy is introduced to make full use of the already-acquired information to guide the VF meetamodeling.
Journal ArticleDOI
Reduced-order models for aerodynamic applications, loads and MDO
Matteo Ripepi,Mark Johannes Verveld,Niklas Karcher,Thomas Franz,Mohammad Abu-Zurayk,Stefan Görtz,Thiemo Kier +6 more
TL;DR: In this article, an overview of reduced-order modeling work performed in the DLR project Digital-X is presented, which is used to predict surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal Article
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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