Identification of characteristic length of microstructure for second order continuum multiscale model by Bayesian neural networks
Abstract
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.
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 test,References
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