Application-driven sequential designs for simulation experiments: Kriging metamodelling

By Jack P. C. Kleijnen, W. V. Beers. Journal of the Operational Research Society, 2004

309 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a1c76bc1b2dfa9ab4112388fa62594553b20ed5e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Achille Messac
2. Souma Chowdhury
3. Ali Mehmani
4. Jianfei Cai
5. Haitao Liu
6. Yew-Soon Ong
7. Wei Shyy
8. Tushar Goel

In Management Science and Operations Research

1. Achille Messac
2. Souma Chowdhury
3. Ali Mehmani
4. Yew-Soon Ong
5. Jianfei Cai
6. Haitao Liu
7. Wei Shyy
8. Tushar Goel

Citations in the corpus

• A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “…common stopping criterion is the successive relative improvement (SRI) measured by the difference of some error criteria, e.g., the cross-validation error, the jackknifing variance (Kleijnen and Van Beers 2004), or the relative absolute error (Kim et al. 2009), in the last several iterations.”
• Comparing error estimation measures for polynomial and kriging approximation of noise-free functions (2009, methodology): “For more details on polynomial response surface approximation, we refer the reader to the texts by Myers and Montgomery (1995) and Khuri and Cornell (1996) .”
• Kriging metamodeling in simulation: A review (2009, methodology): “Sequential procedures for deterministic and random simulations are developed in [28,52] respectively.”
• Valuation of large variable annuity portfolios under nested simulation: A functional data approach (2015, background): “The boundary contracts might be critical to the accuracy of the kriging method (Kleijnen and van Beers, 2004).”
• Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, unknown): “zation [64,91–93].”
• Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, methodology): “…of surrogate-based design, this information can be directly applied for (i) surrogate model refinement (Atamturktur et al. 2013; Jin et al. 2002; Kleijnen and Beers 2004), (ii) surrogate model selection (Chowdhury et al. 2014a; Gorissen et al. 2009; Martin and Simpson 2005), and (iii)…”