Metamodels for simulation input-output relations
WSC '92 Proceedings of the 24th conference on Winter simulation
Design of experiments for fitting subsystem metamodels
Proceedings of the 29th conference on Winter simulation
Regression metamodeling in simulation using Bayesian methods
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
The main issues in nonlinear simulation metamodel estimation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Input modeling: answers to the top ten input modeling questions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Reusing simulation components: simulation software and model reuse: a polemic
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Building credible input models
WSC '04 Proceedings of the 36th conference on Winter simulation
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Optimization by simulation metamodelling methods
WSC '04 Proceedings of the 36th conference on Winter simulation
Sequential design and rational metamodelling
WSC '05 Proceedings of the 37th conference on Winter simulation
A comprehensive review of methods for simulation output analysis
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Sequential designs for simulation experiments: nonlinear regression metamodeling
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Multi-echelon supply chain simulation using metamodel
Proceedings of the 40th Conference on Winter Simulation
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Metamodels are functions with calibrated parameters, used as abstractions and simplifications of the simulation model. A metamodel exposes the system's input-output relationship and can be used as an analysis tool for solving optimization problems or as a surrogate for building blocks in larger scale simulations. Our approach is to analyze statistically the response by modeling the normal distribution mean and variance functions, in order to better depict the problem and improve the knowledge about the system. The metamodel is checked using the confidence intervals of the estimated distribution parameters, and new design points are employed for predictive validation. An example is used to illustrate the development of analysis and surrogate metamodels.