A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics

Archives of Computational Methods in Engineering - Tập 27 - Trang 361-385 - 2019
H. Rappel1,2, L. A. A. Beex1, J. S. Hale1, L. Noels2, S. P. A. Bordas1,3,4
1Institute of Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
2Computational and Multiscale Mechanics of Materials (CM3), Department of Aerospace and Mechanical Engineering, University of Liège, Liège, Belgium
3School of Engineering, Cardiff University, Cardiff, Wales, UK
4Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

Tóm tắt

The aim of this contribution is to explain in a straightforward manner how Bayesian inference can be used to identify material parameters of material models for solids. Bayesian approaches have already been used for this purpose, but most of the literature is not necessarily easy to understand for those new to the field. The reason for this is that most literature focuses either on complex statistical and machine learning concepts and/or on relatively complex mechanical models. In order to introduce the approach as gently as possible, we only focus on stress–strain measurements coming from uniaxial tensile tests and we only treat elastic and elastoplastic material models. Furthermore, the stress–strain measurements are created artificially in order to allow a one-to-one comparison between the true parameter values and the identified parameter distributions.

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