A computational framework for Bayesian inference in plasticity models characterisation

DOI10.1016/j.cma.2017.04.017zbMath1439.74055OpenAlexW2607840364MaRDI QIDQ2309896

P. Stephan Heyns, Erfan Asaadi

Publication date: 6 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2017.04.017

zbMATH Keywords

parameter identificationBayesian inferencenested samplingmaterial characterisationprobabilistic inverse problemmodel class selection

Mathematics Subject Classification ID

Bayesian inference (62F15) Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05) Numerical and other methods in solid mechanics (74S99)

Related Items

Variational Bayesian approximation of inverse problems using sparse precision matrices ⋮ Considerations on the identifiability of fracture and bond properties of reinforced concrete ⋮ On the value of test data for reducing uncertainty in material models: computational framework and application to spherical indentation

Uses Software

• MultiNest
• Bolstad2
• CosmoNest

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
• Principal component analysis.
• Anisotropic plastic deformation of extruded aluminum alloy tube under axial forces and internal pressure
• Inverse analysis for heterogeneous materials and its application to viscoelastic curing polymers
• Bayesian Calibration of Computer Models
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation
• Marginal Likelihood from the Gibbs Output
• Information Theory and Statistical Mechanics
• Probability Theory
• Inverse Problem Theory and Methods for Model Parameter Estimation
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• Structural dynamic analysis using Gaussian process emulators
• Nested sampling for general Bayesian computation

This page was built for publication: A computational framework for Bayesian inference in plasticity models characterisation