A bayesian inference of material parameters from DIC data
Résumé
The framework of the study is to obtain deterministic and probabilistic approximations of the material parameters from measurement informations acquired from digital image correlation data. The deterministic process involves formulation of an optimal control approach. The probabilistic framework inculcates this optimal control method in a Bayesian inference framework. A Markov Chain Monte Carlo sampling method is applied to obtain the posterior probability density function, along with a radial basis function type interpolation for the numerical frugality of the samplings.
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