Identification of characteristic length of microstructure for second order continuum multiscale model by Bayesian neural networks
Keywords:
and acronyms: micro- and macrolevels, second order continuum, computational homogenization (CH), representative volume element (RVE) , finite element method (FEM), Bayesian neural network (BNN), probability density function (pdf), principal component analysis (PCA), indentation testAbstract
This paper deals with the second-order CH of a heterogeneous material undergoing small displacements. Typically, in this approach an RVE of a heterogeneous material is investigated. A given discretized microstructure is determined a priori, without focusing on details of specific discretization techniques. Application of BNN as a tool for identification of characteristic length of a microstructure is discussed. An indentation test was analyzed under plane strain constraints for generating pseudo-experimental patterns by means of FEM. A single input of BNN was formulated due to the application of PCA. The BNN of structure 1-16-1 with sigmoid hidden neurons was designed. The Bayesian inference approach was applied to obtain pdf of the characteristic length. Numerical efficiency of the proposed approach is demonstrated in the paper.
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