A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
| Autor: | Hussein Rappel, Lars Beex, Stéphane Bordas, Jake S Hale, Ludovic Noels |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Mechanical models Applied Mathematics Bayesian probability Uniaxial tension 02 engineering and technology Bayesian inference 01 natural sciences Field (computer science) Computer Science Applications 010101 applied mathematics Bayes' theorem Solid mechanics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0101 mathematics Focus (optics) Algorithm |
| Zdroj: | Archives of Computational Methods in Engineering. 27:361-385 |
| ISSN: | 1886-1784 1134-3060 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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