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- W254394024 abstract "Frequently, important system properties that parameterize system models must be inferred from experimental data in a calibration problem. The quality of the resulting parameter description is directly linked to the quality and quantity of data. Because economic considerations and experimental limitations often lead to data that is sparse and/or imprecise (sources of epistemic uncertainty), this research will characterize parameter uncertainty within a probabilistic framework. A hierarchical system structure will be considered where data may be collected at different levels of varying complexity and cost (e.g. material, component, subsystem, etc.), leading to a tradeoff decision of cost vs. importance of data. In addition to calibration, data is also needed within the proposed framework to perform probabilistic model validation to characterize model uncertainty. This uncertainty will be carried through the system parameters, thereby increasing the conservatism in the prediction because of the presence of imperfect models. This research proposes a constrained discrete optimization formulation for selecting among the candidate data types (calibration or validation and at what level) to determine the respective quantities of observations to collect in order to best quantify parameter uncertainty and model uncertainty (which propagate to prediction uncertainty)." @default.
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- W254394024 date "2014-01-01" @default.
- W254394024 modified "2023-09-24" @default.
- W254394024 title "Optimal Selection of Calibration and Validation Test Samples Under Uncertainty" @default.
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- W254394024 doi "https://doi.org/10.1007/978-3-319-04552-8_39" @default.
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