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
Efficient multi-response adaptive sampling algorithm for construction of variable-fidelity aerodynamic tables
Maxim Tyan,Jae-Woo Lee +1 more
TL;DR: The proposed novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function.
Proceedings ArticleDOI
Variable-Fidelity Surrogate Modeling of Lambda Wing Transonic Aerodynamic Performance
TL;DR: On the whole, variable-f fidelity kriging outperforms varible-fidelity polynomial chaos and monofidelity kriged, and has more favorable properties of training point selection and interpolatory/extrapolatory behavior near the domain boundaries compared to polynometric chaos.
Journal ArticleDOI
Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients
TL;DR: An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement, and the result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget.
Proceedings ArticleDOI
Multi-Fidelity Sparse Polynomial Chaos Surrogate Models for Flutter Database Generation
TL;DR: Multi-fidelity sparse polynomial chaos expansion models of critical flutter dynamic pressures as a function of Mach number, angle of attack, and thickness to chord ratio are constructed in lieu of solely using computationally expensive highfidelity engineering analyses.
Proceedings ArticleDOI
Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison
Chiara Piazzola,Lorenzo Tamellini,Riccardo Pellegrini,Riccardo Broglia,Andrea Serani,Matteo Diez +5 more
TL;DR: The results suggest that MISC could be preferred when only limited data sets are available, and for larger data sets both MISC and SRBF represent a valid option, with a slight preference for SRBF, due to its robustness to noise.
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.
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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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