A hierarchical Bayesian approach for calibration of stochastic material models
نویسندگان
چکیده
Abstract This article recasts the traditional challenge of calibrating a material constitutive model into hierarchical probabilistic framework. We consider Bayesian framework where parameters are assigned distributions, which then updated given experimental data. Importantly, in true engineering setting, we not interested inferring for single experiment, but rather over population possible samples. In doing so, seek to also capture inherent variability from coupon-to-coupon, as well uncertainties around repeatability test. this article, address problem using model. However, vanilla computational approach is prohibitively expensive. Our strategy marginalizes each individual decreasing dimension our inference only hyperparameter—those parameter describing statistics only. marginalization step, requires us derive an approximate likelihood, which, exploit emulator (built offline prior sampling) and quadrature, allowing uncertainty numerical approximation. renders calibration models feasible. The tested two different examples. first compression test simple spring synthetic data; second, more complex example real experiment data fit stochastic elastoplastic 3D-printed steel.
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ژورنال
عنوان ژورنال: Data-centric engineering
سال: 2021
ISSN: ['2632-6736']
DOI: https://doi.org/10.1017/dce.2021.20