Review of Metamodeling Techniques in Support of Engineering Design Optimization

By G. G. Wang, S. Shan. Design Automation Conference, 2007

1,888 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/68d12aeab53834e9e851fa6751b0d3122bf18d51

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In Building and Construction

Citations in the corpus

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “Wang and Shan (2007) listed various design of experiments approaches.”
The evolution, challenges, and future of knowledge representation in product design systems (2013, background): “Wang and Shan [85] list the areas where metamodeling can play a role.”
Ensemble of metamodels with optimized weight factors (2008, background): “In addition, a more recent and extensive review of metamodeling can be found in Wang and Shan (2007) .”
Multiobjective optimization of multi-cell sections for the crashworthiness design (2008, methodology): “The typical statistical parameters used for evaluating the model fitness are the F statistics in terms of R, adjusted Radj and root of meansquared error (RMSE), respectively, as [5,27] F 1⁄4 ðSST SSEÞ=p SSE=ðM p 1Þ , (14)”
A review of machine learning for the optimization of production processes (2019, methodology): “If the machine learning model is supposed to replace a time-consuming physics-based simulation, it is called a meta-or surrogate-model [78, 111].”
Bayesian Optimization for Adaptive Experimental Design: A Review (2020, methodology): “Where the response is complex, non-parametric models are used, including Gaussian Processes, Multivariate Adaptive Regression Splines, and Support Vector Regression [4], [16], [17].”
Crushing analysis and multiobjective crashworthiness optimization of honeycomb-filled single and bitubular polygonal tubes (2011, background): “Many different experimental design methods can be used for sampling points in the design space [40,41].”
Performance study of multi-fidelity gradient enhanced kriging (2015, background): “Jin et al. (2000), Simpson et al. (2001) and Wang and Shan (2006) provided an overview of the most commonly used surrogate modelling techniques.”
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling (2012, background): “This situation may become even worse with increasing problem size [5–7] (increasing number of independent variables and increasing range of these variables).”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “The widely used classical sampling approaches include Full Factorial design, Fractional Factorial design, Central Composite design, Box-Behnken design, and Plackett-Burman design (Wang and Shan 2007).”
An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels (2014, methodology): “So the approximation or metamodeling methods are often used in engineering optimization to save the computational cost [1] for real-world problems.”
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions (2015, background): “Finding the global maximizer of a non-concave objective function based on sequential, noisy observations is a fundamental problem in various real world domains e.g. engineering design [1], ﬁnance [2] and algorithm optimization [3].”
Reliability-Based Design Optimization of Nonlinear Aeroelasticity Problems (2010, background): “Subcritical LCOs require a special treatment in aeroelastic design, not only because they appear before the critical flutter velocity, but also because they lead to discontinuous responses that hamper the use of classical computational design tools for optimization or reliability assessment.”