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.

中文翻译：

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贝叶斯推理的实体力学中的材料参数识别教程

该贡献的目的是直接解释贝叶斯推断如何用于识别固体材料模型的材料参数。贝叶斯方法已经用于此目的，但是对于那些刚接触该领域的人来说，大多数文献并不一定易于理解。原因是大多数文献都集中在复杂的统计和机器学习概念上和/或相对复杂的机械模型上。为了尽可能温和地引入该方法，我们仅关注单轴拉伸试验中的应力-应变测量，并且仅处理弹性和弹塑性材料模型。此外，