Kriging metamodeling in simulation: A review

By Jack P. C. Kleijnen. European Journal of Operational Research, 2009

1,323 citations recorded by the source corpus.

Subfields: Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/e7dcfbfa0f3150ec21386601030ab456e6b72a2f

How researchers see this paper

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

In Management, Monitoring, Policy and Law

Citations in the corpus

Recent Advances in Bayesian Optimization (2022, methodology): “In EGO, a Kriging model, called Design and Analysis of Computer Experiments stochastic process model [157], is adopted to provide best linear unbiased predictions of the objective, which is achieved by minimizing the Mean Squared Error of the predictor [97].”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “developing a non-dynamic mapping (regression) of the complex model inputs into the outputs, namely objective functions and/or constraints (Bieker et al., 2007; Blanning, 1975; Broad et al., 2005; Broad et al., 2010; Gorissen et al., 2010; Keating et al., 2010; Kleijnen, 2009; Laloy et al., 2013; Que”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, background): “, 2009), Kriging (Kleijnen, 2009; Borgonovo et al., 2012; Lamoureux et al., 2014), Gauss process (Rasmussen and Williams, 2006), polynomial chaos expansion (Garcia-Cabrejo and Valocchi, 2014; Formaggia et al.”
A machine learning framework to forecast wave conditions (2017, neutral): “These have alternately been called “metamodels” (Blanning, 1975; Kleijnen, 2009), “model emulators” (O'Hagan, 2006), and “proxy models” (Bieker et al.”
Computational Reduction for Parametrized PDEs: Strategies and Applications (2012, background): “A general reference is given by [13], while a compact survey of these techniques can be found, for instance, in [26].”
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) .”
Remarks on multi-output Gaussian process regression (2018, background): “, small- or largescale regression [4, 5, 6], dimensionality reduction [7], Bayesian optimization [8], uncertainty quantification [9] and time-series analysis [10].”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, methodology): “Kriging [19], radial basis functions [20], and polynomial chaos expansions [21] have also been proposed as metamodeling techniques with applications to uncertainty quantiﬁcation.”
LIF: A new Kriging based learning function and its application to structural reliability analysis (2017, methodology): “Kriging-based methods [27,28] are also widely used in reliability analysis [9,29–34] and Global Optimization [35,36].”
Model-based methods for continuous and discrete global optimization (2017, methodology): “regression models such as polynomial regression or response surface methodology [18], support vector machines (SVM) [19], artificial neural networks [20], radial basis functions [21], or Gaussian process (GP) models, which are sometimes also referred to as DACE or Kriging [22, 23, 24, 25, 26] are th”
Global sensitivity measures from given data (2013, methodology): “…randomised one-at-a-time design of Morris (1991) and sequential bifurcation techniques (Bettonvil and Kleijnen, 1997; Wan et al., 2010), see also Kleijnen (2009a) for an overview), non-parametric or regression-based (Saltelli and Marivoet, 1990; Helton, 1993), variance-based methods (Sobol',…”
Performance study of multi-fidelity gradient enhanced kriging (2015, methodology): “Kriging, which was proposed by Sacks et al. (1989b) for the design and analysis of computer experiments, is arguably popular to approximate deterministic data resulting from computer codes (Sacks et al. 1989a; Kleijnen 2009).”
Tolerance analysis of systems in motion taking into account interactions between deviations (2013, methodology): “Usually, the ANNs use sigmoid functions for approximation purposes.(46) After the training, the network can calculate the correspondent output y to a specified combination of input values xi 34”
Simulation optimization: a review of algorithms and applications (2014, methodology): “samples based on an expected improvement metric [140]. Examples of analogs to this for simulation optimization are provided in [93, 118]. The use of Kriging for simulation metamodeling is explored in [22, 117, 116]. Other criteria that have been used to choose samples are most probable improvement [”