Comparative studies of metamodeling techniques under multiple modeling criteria

By Ruichen Jin, Wei Chen, Timothy W. Simpson. 2000

578 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c5c6dac433c7c311f53844a8681bc44126df97e

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Comparison study on the accuracy of metamodeling technique for non-convex functions (2009, methodology): “[2] claimed that RBF is a better metamodeling technique compared to the other three techniques: Kriging, RSM, and multivariate adaptive regression splines (MARS).”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, background): “[48] confirmed that a consensus among researchers was that experimental designs for deterministic computer”
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.”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “[13] confirmed that a consensus among researchers was that experimental designs for deterministic computer analyses should be space filling.”
Ensemble of metamodels with optimized weight factors (2008, methodology): “ Jin et al. (2001) proposed the use of PRS for slightly nonlinear and noisy responses, while Clarke et al. (2005) found SVR metamodels to be the best in their study.”
Wavelets and their applications (2007, background): “[JIN 00], which presents a work with values of p of around 10 in industrial applications.”
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, methodology): “In a comparative study of different meta-modeling techniques (Y. Jin et al., 2000), it is explicitly stated that multiple metrics such as accuracy, robustness, efficiency, transparency, and conceptual simplicity should be used for meta-model assessment.”
Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points (2003, methodology): “However, much more insight on various models has been gained recently [11,21-25].”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, methodology): “Major surrogate modeling methods include Polynomial Response Surfaces (Jin et al. 2000), Kriging (Simpson et al.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “Jin et al. (2001) compared four models (polynomial regression, multivariate adaptive regression splines, radial basis functions, and Kriging model), and Wang et al. (2006) compared meta-models (multivariate adaptive regression splines, radial basis functions , adaptive weighted least squares,…”
Development of gradient-enhanced kriging approximations for multidisciplinary design optimization (2003, methodology): “In optimization, the smoothing capability of RSM allows quick convergence of noisy functions[15].”
Comparison of surrogate models for turbomachinery design (2008, background): “published a description of almost the same types of surrogate models and also a comparison of their efficiency for mathematical functions [15].”