Journal Article10.1080/0305215X.2014.918114
Difference mapping method using least square support vector regression for variable-fidelity metamodelling
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TL;DR: A difference mapping method using least square support vector regression is developed in this work, as a special metamodelling methodology that includes variable-fidelity data, to replace the computationally expensive computer codes.
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Abstract: Engineering design, especially for complex engineering systems, is usually a time-consuming process involving computation-intensive computer-based simulation and analysis methods. A difference mapping method using least square support vector regression is developed in this work, as a special metamodelling methodology that includes variable-fidelity data, to replace the computationally expensive computer codes. A general difference mapping framework is proposed where a surrogate base is first created, then the approximation is gained by a mapping the difference between the base and the real high-fidelity response surface. The least square support vector regression is adopted to accomplish the mapping. Two different sampling strategies, nested and non-nested design of experiments, are conducted to explore their respective effects on modelling accuracy. Different sample sizes and three approximation performance measures of accuracy are considered.
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Citations
•Book
Annual Review of Fluid Mechanics
W. R. Sears,Milton van Dyke,J. V. Wehausen,John L. Lumley,H.L. Reed,Stephen H. Davis,Parviz Moin +6 more
- 01 Jan 1994
TL;DR: In this paper, recent developments in three dimensional and unsteady turbulence boundary layer computations are discussed, including the physics of convention solidification interaction, the continental shelf bottom boundary layer, gravity currents in rotating systems, eddies, waves, circulation, and mixing.
470
Issues in Deciding Whether to Use Multifidelity Surrogates
TL;DR: In this article, it is shown that multifidelity surrogates are essential in cases where it is not affordable to have more than a few high-fidelity samples, but it is affordable to have as many low-idelity samples as needed.
187
Review of Multi-fidelity Models
30 Apr 2023
TL;DR: Multi-fidelity models as mentioned in this paper integrate high fidelity and low fidelity models to obtain fast yet accurate predictions to obtain high accuracy and low computational cost, however, the savings achieved through these models depend highly on the problem.
130
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.
72
A robust optimization approach based on multi-fidelity metamodel
TL;DR: A MF metamodel assisted robust optimization approach is developed, in which the interpolation uncertainty of the MF metAModel and design variable uncertainty are quantified and taken into consideration and can obtain a solution that is both optimal and within the feasible region even with perturbation of the uncertain variables.
65
References
Design and analysis of computer experiments
Sonja Kuhnt,David M. Steinberg +1 more
TL;DR: The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.
Comparative studies of metamodelling techniques under multiple modelling criteria
TL;DR: This paper systematically compare four popular metamodelling techniques – polynomial regression, multivariate adaptive regression splines, radial basis functions, and kriging – based on multiple performance criteria using fourteen test problems representing different classes of problems.
1.7K
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Gongming Wang,Songqing Shan +1 more
- 01 Jan 2006
TL;DR: This work reviews the state-of-the-art metamodel-based techniques from a practitioner's perspective according to the role of meetamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems.
1.6K
Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses
Stella M. Clarke,Jan Griebsch,Timothy W. Simpson +2 more
- 01 Jan 2003
TL;DR: This paper investigates support vector regression (SVR) as an alternative technique for approximating complex engineering analyses and shows great potential for metamodeling applications, adding to the growing body of promising empirical performance of SVR.
570
•Book
Annual Review of Fluid Mechanics
W. R. Sears,Milton van Dyke,J. V. Wehausen,John L. Lumley,H.L. Reed,Stephen H. Davis,Parviz Moin +6 more
- 01 Jan 1994
TL;DR: In this paper, recent developments in three dimensional and unsteady turbulence boundary layer computations are discussed, including the physics of convention solidification interaction, the continental shelf bottom boundary layer, gravity currents in rotating systems, eddies, waves, circulation, and mixing.
470