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
Wing jig shape optimisation with gradient-assisted metamodel building in a trust-region optimisation framework
TL;DR: In this paper , the authors presented the latest developments in the multipoint approximation method (MAM) based on a gradient-assisted metamodel assembly technique within a trust-region optimisation framework.
Proceedings ArticleDOI
Multi-Fidelity Kriging and Sparse Polynomial Chaos Surrogate Models for Uncertainty Quantification
TL;DR: In this paper , a multi-fidelity sparse polynomial chaos expansion (SPCE), kriging as well as a combination of the two surrogate approaches into a multifidelity SPCE-Kriging model are constructed for inexpensive uncertainty quantification.
Journal ArticleDOI
An optimization method for material sound absorption performance based on surrogate model
TL;DR: In this article , a set of general optimization algorithm framework suitable for the surrogate model and the gradient-enhanced Kriging model (GEK) was developed, based on which the evolution of the sound absorption coefficient of the anechoic structure under three different working conditions (100 −10 000 Hz, 100 −1500 Hz and 100 − 10 000 Hz under static pressure) was compared.
Journal ArticleDOI
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification
TL;DR: In this article , a parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems.
Journal ArticleDOI
A modified trust-region assisted variable-fidelity optimization framework for computationally expensive problems
Chengshan Li,Huachao Dong +1 more
TL;DR: A novel modified trust-region assisted variable-fidelity optimization (MTR-VFO) framework is presented which is verified to be more effective than some existing typical methods and shows great potential of solving computationally expensive problems effectively.
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.
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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