Journal ArticleDOI
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
Reads0
Chats0
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
More filters
Journal ArticleDOI
Large scale variable fidelity surrogate modeling
TL;DR: Two approaches to circumvent computational burden of the Gaussian process regression framework are proposed, one based on the Nyström approximation of sample covariance matrices and another based on an intelligent usage of a blackbox that can evaluate a low fidelity function on the fly at any point of a design space.
Journal ArticleDOI
All-at-once approach to multifidelity polynomial chaos expansion surrogate modeling
TL;DR: In cases where polynomials are suitable approximations to the true function, the new all-at-once approach is found to reduce error in the surrogate faster than the method of weighted combinations.
Journal ArticleDOI
Unified Framework for Training Point Selection and Error Estimation for Surrogate Models
TL;DR: The results show that the proposed training point selection approach improves the convergence monotonicity and produces more accurate surrogate models compared to random and quasi-randomTraining point selection strategies.
Journal ArticleDOI
Gradient based hyper-parameter optimisation for well conditioned kriging metamodels
TL;DR: In this paper, a two-step approach to efficiently carry out hyper parameter optimisation, required for building kriging and gradient enhanced metamodels, is presented, making use of an initial line search along the hyper-diagonal of the design space in order to find a suitable starting point for a subsequent gradient based optimisation algorithm.
Journal ArticleDOI
An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging
TL;DR: An adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model and shows that it provides a more accurate metamodel at the same simulation cost, which is very important in metAModel-based engineering design problems.
References
More filters
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.
Related Papers (5)
Efficient Global Optimization of Expensive Black-Box Functions
Predicting the output from a complex computer code when fast approximations are available
Marc C. Kennedy,Anthony O'Hagan +1 more