A Bayesian inference approach for parametric identification through optimal control method

Abstract

AbstractThe objective of the research is to obtain deterministic and probabilistic resolution of material parameters from full field measurements of kinematic data acquired from digital image correlation. The deterministic inverse problem involves formulation of an optimal control approach where the complete knowledge of the boundary conditions and the measurement data are not required. 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 network for the numerical frugality of the samplings.