Simultaneous identification of the parameters in the plasticity function for power hardening materials : A Bayesian approach

TL;DR

This paper introduces a Bayesian method for simultaneously estimating key parameters in the plasticity function of power hardening materials, improving accuracy in inverse problems with noisy data.

Contribution

It develops a Bayesian framework for the inverse problem of parameter identification in plasticity, combining iterative solutions with numerical methods for enhanced accuracy.

Findings

Effective parameter estimation with noisy data

Bayesian approach improves inverse problem solutions

Numerical examples demonstrate method applicability

Abstract

In this paper, we study simultaneous determination of the strain hardening exponent, the shear modulus and the yield stress in an inverse problem. First, we analyze the direct and the inverse problems. Then we formulate the inverse problem in the Bayesian framework. After solving the direct problem by an iterative approach, we propose a numerical method based on a Bayesian approach for the numerical solution of the inverse problem. Numerical examples with noisy data illustrate applicability and accuracy of the proposed method to some extent.\