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A deep learning driven uncertain full‐field homogenization method
Author(s) -
Henkes Alexander,
Caylak Ismail,
Mahnken Rolf
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000180
Subject(s) - homogenization (climate) , polynomial chaos , monte carlo method , multivariate statistics , uncertainty quantification , artificial neural network , fourier transform , computer science , volume fraction , representative elementary volume , mathematics , mathematical optimization , microstructure , algorithm , materials science , mathematical analysis , artificial intelligence , composite material , machine learning , statistics , biodiversity , ecology , biology
This work is directed to uncertainty quantification of homogenized effective properties of composite materials with a complex, three dimensional microstructure. The uncertainties arise in the material parameters of the single constituents as well as in the fiber volume fraction. They are taken into account by multivariate random variables. Uncertainty quantification is carried out by an efficient surrogate model based on pseudospectral polynomial chaos expansion and artificial neural networks, which is trained on a fast Fourier transformation based homogenization method. The numerical example deals with the comparison of the presented method to Monte Carlo‐type simulation for uncertain homogenization of spherical inclusions in a matrix material.