Journal ArticleDOI
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Gongming Wang,Songqing Shan +1 more
- Vol. 129, Iss: 4, pp 370-380
Reads0
Chats0
TLDR
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.Abstract:
Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.Copyright © 2006 by ASMEread more
Citations
More filters
Journal ArticleDOI
Variable-Fidelity Electromagnetic Simulations and Co-Kriging for Accurate Modeling of Antennas
TL;DR: This paper describes the low-cost antenna modeling methodology involving variable-fidelity electromagnetic (EM) simulations and co-Kriging, which exploits sparsely sampled accurate EM data as well as densely sampled coarse-discretization EM simulations that are accommodated into one model using the co- Kriging technique.
Journal ArticleDOI
A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains
TL;DR: A two-phase differential evolution for uniform designs in constrained experimental domains using a clustering DE to guide the population toward the constrained experimental domain from different directions promptly and maximizing the minimum Euclidean distance among samples is treated as another fitness function.
Journal ArticleDOI
Reducing the computational cost of automatic calibration through model preemption
Saman Razavi,Bryan A. Tolson,L. Shawn Matott,Neil R. Thomson,Angela MacLean,Frank Seglenieks +5 more
TL;DR: Deterministic preemption–enabled calibration algorithms which make no approximations to the mathematical simulation model are a simple alternative to the increasingly common and more complex approach of metamodeling for computationally constrained model calibration.
Journal ArticleDOI
Multifidelity surrogate modeling based on radial basis functions
TL;DR: A multifidelity metamodeling method based on Radial Basis Function, the co-RBF, is proposed and this surrogate model is compared with the classical co-kriging method on two analytical benchmarks and on the photoacoustic gas sensor.
Journal ArticleDOI
Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions
TL;DR: A novel approach to predict energy demand variation with a wide range of uncertain factors based on a previously developed numerical simulation model, which revealed uncertainty in temperature, rolling resistance and payload contributed most to the variation in energy demand.
References
More filters
Book
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
TL;DR: Using a practical approach, this book discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques.
Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
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.