Design of Experiments within the MÖbius Modeling Environment

Models of complex systems often contain model parameters for important rates, probabilities, and initial state values. By varying the parameter values, the system modeler can study the behavior of the system under a wide range of system and environmental assumptions. However, exhaustive exploration...

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Bibliographic Details
Main Authors: Courtney, T., Gaonkar, S., McQuinn, M.G., Rozier, E., Sanders, W.H., Webster, P.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data security
Design for experiments
Design optimization
Discrete event systems
Lead
Numerical models
Performance analysis
Predictive models
Response surface methodology
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Summary:Models of complex systems often contain model parameters for important rates, probabilities, and initial state values. By varying the parameter values, the system modeler can study the behavior of the system under a wide range of system and environmental assumptions. However, exhaustive exploration of the parameter space of a large model is computationally expensive. Design of experiments techniques provide information about the degree of sensitivity of output variables to various input parameters. Design of experiments makes it possible to find parameter values that optimize measured outputs of the system by running fewer experiments than required by less rigorous techniques. This paper describes the design of experiments techniques that have been integrated in the MÖbius tool.
DOI:10.1109/QEST.2007.36