A probabilistic framework is presented for assessing the uncertainties in simulation predictions that arise from model parameters derived from uncertain measurements. A probabilistic network facilitates both conceptualizing and computationally implementing an analysis of a large number of experiments in terms of many intrinsic models in a logically consistent manner. This approach permits one to improve one's knowledge about the underlying models at every level of the hierarchy of validation experiments.
Keywords: uncertainty analysis, error analysis, simulation code, validation, probabilistic network, model checking, adjoint differentiation
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