THIS IS A WORK IN PROGRESS!
This repository contains Jupyter Notebooks to recreate the examples published in the following paper (link):
Rappel, H., Beex, L. A., Hale, J. S., Noels, L., & Bordas, S. P. A. (2020). A tutorial on Bayesian inference to identify material parameters in solid mechanics. Archives of Computational Methods in Engineering, 27(2), 361-385.
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.
There are four examples:
- Linear Elasticity
- Linear Elasticity-Perfect Plasticity
- Linear Elasticity-Linear Hardening
- Linear Elasticity-Nonlinear Hardening
If you spot any mistakes then please raise an issue or if you would prefer you can contact me using the following email address: