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Taylor Kriging Metamodeling for Stochastic Simulation Interpolation

Heping Liu, Yanli Chen
2011 International Journal of Operations Research and Information Systems  
Discuss the Kriging interpolation for stochastic simulation and develop Detrended Kriging (Van Beers & Kleijnen, 2004).  ...  The early application of Kriging to stochastic simulations was proposed by Barton (1994) . In his introduction to Kriging, he regards Kriging as a spatial correlation metamodel.  ... 
doi:10.4018/joris.2011010104 fatcat:icds33y4prax3fdha7wo6lznkm

Recent developments in metamodel based robust black-box simulation optimization: An overview

Nader Ale Ebrahim, Amir Parnianifard, A Azfanizam, M Ariffin, M Ismail
2018 Figshare  
In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation  ...  At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.  ...  Ale Ebrahim, "Recent developments in metamodel based robust black-box simulation optimization: An overview," Decision Science Letters, vol. 8, no. 1, pp. 17-44, 2019.  ... 
doi:10.6084/m9.figshare.6466625 fatcat:va7fpxohdvgn7o4boceztyqupe

Recent developments in metamodel based robust black-box simulation optimization: An overview

Amir Parnianifard, A.S. Azfanizam, M.K.A. Ariffin, M.I.S. Ismail, Nader Ale Ebrahim
2019 Decision Science Letters  
This paper covered more the stochastic simulation-optimization hybrid metamodels (e.g. polynomial regression and Kriging).  ...  Kriging is a popular method for metamodeling with simulation data (Havinga et al., 2017) .  ... 
doi:10.5267/j.dsl.2018.5.004 fatcat:j5ihfxwnlfesbcsuyyli622b44

An overview of the design and analysis of simulation experiments for sensitivity analysis

Jack P.C. Kleijnen
2005 European Journal of Operational Research  
Their I/O data are analyzed through second-order polynomials for group screening, and through Kriging models for LHS.  ...  Modern designs were developed for simulated systems in engineering, management science, etc.  ...  Section 4 introduces Kriging metamodels, which provide exact interpolation in deterministic simulation. These metamodels often use space-filling designs, such as Latin hypercube sampling (LHS).  ... 
doi:10.1016/j.ejor.2004.02.005 fatcat:gkfskjv5fvhyxntajph24igeum

An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis

Jack P. C. Kleijnen
2004 Social Science Research Network  
Their I/O data are analyzed through second-order polynomials for group screening, and through Kriging models for LHS.  ...  Modern designs were developed for simulated systems in engineering, management science, etc.  ...  Section 4 introduces Kriging metamodels, which provide exact interpolation in deterministic simulation. These metamodels often use space-filling designs, such as Latin hypercube sampling (LHS).  ... 
doi:10.2139/ssrn.557741 fatcat:mgdtswdjpfb3tlfg5nvpp6p77a

An algorithm for the use of surrogate models in modular flowsheet optimization

José A. Caballero, Ignacio E. Grossmann
2008 AIChE Journal  
The black box modules are substituted by metamodels based on a kriging interpolation that assumes that the errors are not independent but a function of the independent variables.  ...  A Kriging metamodel uses a non Euclidean measure of distance that avoid sensitivity to the units of measure.  ...  Kriging Metamodels in Modular Chemical Process Simulators Modular Chemical Process Simulators are widely used tools used by chemical process industries.  ... 
doi:10.1002/aic.11579 fatcat:2gsz6mz7yjcxfk5grghuccushu

Robust Optimization in Simulation: Taguchi and Krige Combined

Gabriella Dellino, Jack P. C. Kleijnen, Carlo Meloni
2009 Social Science Research Network  
we use bootstrapping to quantify the variability in the estimated Kriging metamodels.  ...  O ptimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments.  ...  Meloni thank CentER for the financial support when they visited Tilburg University. G.  ... 
doi:10.2139/ssrn.1492134 fatcat:y6x7qva7rnfvhn2cyzrlrq4dji

Robust Optimization in Simulation: Taguchi and Krige Combined

Gabriella Dellino, Jack P. C. Kleijnen, Carlo Meloni
2012 INFORMS journal on computing  
we use bootstrapping to quantify the variability in the estimated Kriging metamodels.  ...  O ptimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments.  ...  Meloni thank CentER for the financial support when they visited Tilburg University. G.  ... 
doi:10.1287/ijoc.1110.0465 fatcat:ztd6ibudpndb7dh5ugfdkm3o7m

Quantifying Input Uncertainty via Simulation Confidence Intervals

Russell R. Barton, Barry L. Nelson, Wei Xie
2014 INFORMS journal on computing  
WeiXie2013@u.northwestern.edu} W e consider the problem of deriving confidence intervals for the mean response of a system that is represented by a stochastic simulation whose parametric input models have  ...  We develop a metamodel strategy and associated experiment design method that avoid the need for low-order approximation to the response and that minimizes the impact of intrinsic (simulation) error on  ...  Our proposal is to use stochastic kriging for the metamodel, and input-distribution moments as the independent variables.  ... 
doi:10.1287/ijoc.2013.0548 fatcat:vnkwifwejrd3jinv2gbvgwzkw4

Design and Analysis of Monte Carlo Experiments [chapter]

Jack P. C. Kleijnen
2011 Handbook of Computational Statistics  
Note that in deterministic simulation, Kriging has an important advantage over regression analysis: Kriging is an exact interpolator; that is, predicted values at observed input values are exactly equal  ...  Such Kriging basics Kriging is an interpolation method that predicts unknown values of a random process; see the classic textbook on Kriging in spatial statistics, Cressie (1993).  ... 
doi:10.1007/978-3-642-21551-3_18 fatcat:2s4f4aszi5fnhaq7ob3bqf4aoy

Design and Analysis of Monte Carlo Experiments

Jack P. C. Kleijnen
2004 Social Science Research Network  
Note that in deterministic simulation, Kriging has an important advantage over regression analysis: Kriging is an exact interpolator; that is, predicted values at observed input values are exactly equal  ...  Such Kriging basics Kriging is an interpolation method that predicts unknown values of a random process; see the classic textbook on Kriging in spatial statistics, Cressie (1993).  ... 
doi:10.2139/ssrn.557742 fatcat:2yhrr4vvtzgn3l5mduce2dghhi

On the use of second-order derivatives and metamodel-based Monte-Carlo for uncertainty estimation in aerodynamics

M. Martinelli, R. Duvigneau
2010 Computers & Fluids  
Secondly, metamodelling techniques (radial basis functions, kriging) are employed in conjunction with Monte-Carlo simulations to derive statistical information.  ...  In this article, we present and compare two methods to account for uncertainty in aerodynamic simulation.  ...  Kriging and radial basis functions metamodels have been used in conjunction with Monte-Carlo simulations to estimate statistics.  ... 
doi:10.1016/j.compfluid.2010.01.007 fatcat:brsvhrcf4refpavw4zr7fvqiyu

A framework for input uncertainty analysis

Russell R. Barton, Barry L. Nelson, Wei Xie
2010 Proceedings of the 2010 Winter Simulation Conference  
Therefore, we want the confidence interval to account for both uncertainty about the input models and stochastic noise in the simulation output; standard practice only accounts for the stochastic noise  ...  We consider the problem of producing confidence intervals for the mean response of a system represented by a stochastic simulation that is driven by input models that have been estimated from "real-world  ...  Stochastic kriging is an extension of kriging for deterministic computer experiments to stochastic simulation.  ... 
doi:10.1109/wsc.2010.5679071 dblp:conf/wsc/BartonNX10 fatcat:hkgps7wsqbd2hpzfvo5dprc3pa

Expected improvement in efficient global optimization through bootstrapped kriging

Jack P. C. Kleijnen, Wim van Beers, Inneke van Nieuwenhuyse
2011 Journal of Global Optimization  
This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling);  ...  this Kriging is used to analyze the input/output data of the simulation model (computer code).  ...  We also thank Emmanuel Vazquez (SUPÉLEC) for bringing Abt (1999) and Müller and Pronzato (2009) to our attention.  ... 
doi:10.1007/s10898-011-9741-y fatcat:ahmwttqzejey5gzohpamgaf7lm

Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment

Zhiyi Wang, Nicola Pedroni, Irmela Zentner, Enrico Zio
2018 Engineering structures  
In this paper, an artificial neural network (ANN) is constructed to improve the computational efficiency for the calculation of structural outputs.  ...  Acknowledgement The authors want to thank the two anonymous reviewers for their valuable comments to this work.  ...  Several conclusions can be drawn from Table 4 : i) Kriging interpolation is not an appropriate metamodel for this study, since the test error is much larger than other models.  ... 
doi:10.1016/j.engstruct.2018.02.024 fatcat:uwogbeqbqzbtbfl4lden6lkhlq
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