A methodology for fitting and validating metamodels in simulation

Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation.For this metamodeling, a process with thirteen steps is proposed.To check the expressiveness and the completeness aspect of the metamodel, we validate this representational metamodel by analysing a validation over ten well-known disaster management metamodels which are chosen based on a Othman S. (2010) A Disaster Management Metamodel (DMM) Validated. (eds) Knowledge Management and Acquisition for Smart Systems and Services. This paper proposes a methodology that replaces the usual ad hoc approach to metamodeling.The methodology consists of a metamodeling process with 10 steps.This process includes classic design of experiments (DOE) and measuring fit through standard measures such as -square and cross-validation statistics.Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures.This DOE is extended to sequential or stagewise DOE.

A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model.Simulation of a system is represented as the running of the system's model.The automotive industry designs, develops, manufactures, markets, and sells motor vehicles, and is one of the Earth's most important economic sectors by revenue.A model conforms to its metamodel in the way that a computer program conforms to the grammar of the programming language in which it is written.Various types of metamodels include polynomial equations, neural network, Kriging, etc.

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