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• W2890518931 abstract "We discuss Bayesian inference for the identification of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference between the model and the experimental data may still be present if the data is not contaminated by noise, we also incorporate the possible error of the model itself. The three formulations to describe model uncertainty in this contribution are: (1) a random variable which is taken from a normal distribution with constant parameters, (2) a random variable which is taken from a normal distribution with an input-dependent mean, and (3) a Gaussian random process with a stationary covariance function. Our results show that incorporating model uncertainty often, but not always, improves the results. If the error in the strain is considered as well, the results improve even more." @default.
• W2890518931 created "2018-09-27" @default.
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• W2890518931 date "2019-01-01" @default.
• W2890518931 modified "2023-10-18" @default.
• W2890518931 title "Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty" @default.
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• W2890518931 doi "https://doi.org/10.1016/j.probengmech.2018.08.004" @default.
• W2890518931 hasPublicationYear "2019" @default.
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