A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network
Author:
Publisher
Elsevier BV
Subject
Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics
Reference72 articles.
1. Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators;Soize;Comput. Methods Appl. Mech. Engrg.,2006
2. Tensor-valued random fields for meso-scale stochastic model of anisotropic elastic microstructure and probabilistic analysis of representative volume element size;Soize;Probab. Eng. Mech.,2008
3. Multiscale identification of the random elasticity field at mesoscale of a heterogeneous microstructure using multiscale experimental observations;Nguyen;Int. J. Multiscale Comput. Eng.,2015
4. Robust multiscale identification of apparent elastic properties at mesoscale for random heterogeneous materials with multiscale field measurements;Zhang;Materials,2020
5. Maximum likelihood estimation of stochastic chaos representations from experimental data;Desceliers;Internat. J. Numer. Methods Engrg.,2006
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