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• W2278536479 abstract "A new manifold-based reduced order model for nonlinear problems in multiscale modeling of heterogeneous hyperelastic materials is presented. The model relies on a global geometric framework for nonlinear dimensionality reduction (Isomap), and the macroscopic loading parameters are linked to the reduced space using a Neural Network. The proposed model provides both homogenization and localization of the multiscale solution in the context of computational homogenization. To construct the manifold, we perform a number of large three-dimensional simulations of a statistically representative unit cell using a parallel finite strain finite element solver. The manifold-based reduced order model is verified using common principles from the machine-learning community. Both homogenization and localization of the multiscale solution are demonstrated on a large three-dimensional example and the local microscopic fields as well as the homogenized macroscopic potential are obtained with acceptable engineering accuracy." @default.
• W2278536479 created "2016-06-24" @default.
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• W2278536479 date "2016-05-01" @default.
• W2278536479 modified "2023-10-16" @default.
• W2278536479 title "A nonlinear manifold-based reduced order model for multiscale analysis of heterogeneous hyperelastic materials" @default.
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• W2278536479 doi "https://doi.org/10.1016/j.jcp.2016.01.040" @default.
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