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Simulation model validation is an important part of simulation development and use. An emerging challenge is the examination of functional data systems and the validation of the simulations built to represent them. While validation methods do exist, there is a gap in the engineering and statistical approaches used for functional model validation. This case study demonstrates the use of recently developed methods based on the use of wavelets to bridge the engineering‐statistical gap in functional simulation model validation. Two methods are used to provide insight regarding model validity, and a third method is used to identify areas of system‐simulation disagreement when model validity fails to hold.
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