Regression and Kriging metamodels with their experimental designs in simulation: A review

By Jack P. C. Kleijnen. European Journal of Operational Research, 2017

316 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management of Technology and Innovation, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c2dca5c54cc4273c803f5a6b83079f12c19c4567

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. Warrren B Powell
2. R. Matousek
3. Jakub Kudela

In Management of Technology and Innovation

1. Warrren B Powell
2. R. Matousek
3. Jakub Kudela

In Mechanical Engineering

1. Warrren B Powell
2. R. Matousek
3. Jakub Kudela

Citations in the corpus

• A unified framework for stochastic optimization (2019, background): “Kleijnen (2017) reviews regression and riging metamodels for simulation models, which is the foundaion of most stochastic optimization.”
• Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “…are calculated in a different way to their Kriging and RBF counterparts, yet they contribute to the surrogate prediction in the exact same manner: Table 2 Frequently employed correlation models for Kriging surrogates (Kleijnen 2017) Name Correlation model Exponential ψ j (θ,m j ) = exp(−θ j | m j”
• Expert Systems With Applications (unknown): “To assess the relative competitiveness of the proposed MPTree in terms of prediction accuracy, we compare the proposed MPTree to a number of popular regression methods in literature, including CART, M5’, Cubist, linear regression, SVR, MLP, Kriging, KNN, MARS, segmented regression ( Yang et al., 201”