Comparative studies of metamodelling techniques under multiple modelling criteria

By Ruichen Jin, Wei Chen, Timothy W. Simpson. 2001

1,644 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/ec5e23158b777d4d74a4fd8b81c9094c96189399

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Plant Science

Citations in the corpus

On polynomial response surfaces and Kriging for use in structural optimization of crashworthiness (2005, methodology): “Some comparative studies of different response surface approximation techniques have been performed earlier, cf. Giunta et al. (1998), Gu (2001), Jin et al. (2001) , Simpson et al. (1998) and Yang et al. (2001b).”
Managing computational complexity using surrogate models: a critical review (2020, background): “Kleijnen (2009) reviews kriging and its applications in RSM. Jin et al. (2001) and Chen et al. (2006) .”
Incremental modeling of a new high-order polynomial surrogate model (2016, background): “After the order increment, the polynomial with the largest adjusted R-square is determined as the final HOPSM.”
An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels (2014, methodology): “[8] studied four popular metamodeling techniques, including the polynomial regression, Kriging model, MARS, and RBF based on multiple performance criteria.”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, methodology): “Before using a particular metamodelling technique it is essential to check it rigorously for its quality of fitting and prediction capability [121-123].”
The promise and peril of uncertainty quantification using response surface approximations (2005, background): “In addition, MARS has received some use in the engineering community (cf. Jin et al. 2001) and we were interested in exploring both its utility and its drawbacks.”
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,…”
Model-based methods for continuous and discrete global optimization (2017, background): “ally, surrogate models are assessed and chosen according to their estimated true error [54, 55, 2].”
Kriging Hyperparameter Tuning Strategies (2008, background): “the variance is ( 10 ) and the concentrated likelihood function is given by”
Probabilistic characterisation for dynamics and stability of laminated soft core sandwich plates (2017, methodology): “The mesh-free moving Kriging interpolation method is presented by Bui et al. [27] for analysis of natural frequencies of laminated composite plates while Yang et al. [28] studied the vibration and damping analysis of thick sandwich cylindrical shells with a viscoelastic core.”
Global response approximation with radial basis functions (2006, methodology): “In the work by Jin et al. (2001), they compared RBF models created by the linear basis function with the RSM and Kriging models, and found that RBF models were the best.”