Stochastic kriging for simulation metamodeling

By Bruce E. Ankenman, Barry L. Nelson, Jeremy Staum. Online World Conference on Soft Computing in Industrial Applications, 2008

747 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3fe7238de8a02406f90e31ded650e0b2ff0cda4d

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In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

A benchmark of kriging-based infill criteria for noisy optimization (2013, background): “Examples of such simulators can be found in a wide range of applications, including nuclear safety assessment [9], discrete event simulation [1], acoustic wave propagation in turbulent fluids [17], airfoil optimization [23], design of composite materials [32, 31] and experimental measurements in mec”
Stochastic natural frequency of composite conical shells (2015, methodology): “[12] employed the Kriging approach for stochastic simulation bymeta-modelling.”
Monte Carlo sampling-based methods for stochastic optimization (2014, background): “[8], Chick and Frazier [47], Chick and Inoue [48], Frazier et al.”
Cases for the nugget in modeling computer experiments (2010, background): “Examples abound in biology (Johnson, 2008; Henderson et al., 2009), chemistry (Gillespie, 2001), and industrial design and engineering (Ankenman et al., 2009) to name just a few.”
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “Kriging is a general and statistically rigorous method for interpolating deterministic [92,110,111] or even stochastic [113] computer simulations.”
Review of multi-fidelity models (2016, methodology): “Gaussian process models that employ a non-zero nugget effect fall under NDMs [11, 180].”
The Complexities of Agent-Based Modeling Output Analysis (2015, background): “Research into DOE methods for ABMs is still evolving (Klein et al. 2005; Kleijnen et al. 2005; Ankenman et al. 2008; Lorscheid et al. 2012).”
Semiconvex Regression for Metamodeling-Based Optimization (2014, methodology): “Response surfaces are metamodels fit in a traditionally online manner using regression methods such as second order parametric models [12], radial basis functions [64], neural networks [26] or stochastic kriging [5]; see [6] for a review.”
Handbook of Simulation Optimization (2014, methodology): “Many reviews on R&S are available [1, 29, 39].”