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
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
TL;DR: In many situations across computational science and engineering, multiple computational models are available that describe a system of interest as discussed by the authors, and these different models have varying evaluation costs, i.e.
Journal ArticleDOI
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
Zhong-Hua Han,Stefan Görtz +1 more
TL;DR: It is observed that hierarchical kriging provides a more reasonable mean-squared-error estimation than traditional cokriging and can be applied to the efficient aerodynamic analysis and shape optimization of aircraft or anywhere where computer codes of varying fidelity are in use.
Journal ArticleDOI
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design
TL;DR: This article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design and discusses some important issues that affect the success of an adaptive sampling approach.
Journal ArticleDOI
Review of multi-fidelity models
TL;DR: It is found that time savings are highly problem dependent and that MFM methods provided time savings up to 90% and guidelines for authors to present their MFM savings in a way that is useful to future MFM users are included.
Journal ArticleDOI
A Python surrogate modeling framework with derivatives
Mohamed Amine Bouhlel,John T. Hwang,Nathalie Bartoli,Rémi Lafage,Joseph Morlier,Joaquim R. R. A. Martins +5 more
TL;DR: The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions that provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
References
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Proceedings ArticleDOI
Optimization with variable-fidelity models applied to wing design
TL;DR: Three versions of AMF, based on three nonlinear programming algorithms, are demonstrated on a 3D aerodynamic wing optimization problem and a 2D airfoil optimization problem, and preliminary results indicate threefold savings in terms of high-fidelity analyses in case of the 3D problem and twofold savings for the 2D problem.
Journal ArticleDOI
Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging
J. Laurenceau,Pierre Sagaut +1 more
TL;DR: The number of samples needed to have a globally accurate surface stays generally out of reach for problems considering more than four design variables.
Proceedings ArticleDOI
Second-Order Corrections for Surrogate-Based Optimization with Model Hierarchies
TL;DR: It is demonstrated that first-order consistency can be insufficient to achieve acceptable convergence rates in practice and new second-order additive, multiplicative, and combined corrections which can significantly accelerate convergence are presented.
Proceedings ArticleDOI
Using gradients to construct cokriging approximation models for high-dimensional design optimization problems
H.-S. Chung,Juan J. Alonso +1 more
TL;DR: The Cokriging method, an extension of Kriging, which can incorporate secondary information such as values of gradients in addition to primary function values of the sample points has been utilized for constructing approximation models in a realistic design optimization process.
Journal ArticleDOI
Discrete Adjoint-Based Approach for Optimization Problems on Three-Dimensional Unstructured Meshes
TL;DR: A comprehensive strategy for developing and implementing discrete adjoint methods for aerodynamic shape optimization problems is presented, and the adjoint of the complete optimization problem, including flow equations and mesh motion equations is constructed in a modular and verifiable fashion.
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