Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
https://orcid.org/0000-0001-7216-861X
https://orcid.org/0000-0001-8634-7002
Abstract 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.
A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
https://orcid.org/0000-0001-7216-861X
https://orcid.org/0000-0001-8634-7002
Abstract 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.
A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
https://orcid.org/0000-0001-7216-861X
https://orcid.org/0000-0001-8634-7002
Abstract 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.
A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
Rappel, H. (Autor:in) / Beex, L. A. A. (Autor:in) / Hale, J. S. (Autor:in) / Noels, L. (Autor:in) / Bordas, S. P. A. (Autor:in)
2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
| Springer Verlag | 2020
A Tutorial in Bayesian Potential Outcomes Mediation Analysis
| British Library Online Contents | 2018
Bayesian inference of the spatial distributions of material properties
| British Library Online Contents | 2018
Modeling glulams in linear range with parameters updated using Bayesian inference
| Online Contents | 2017