Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

This paper extends the deep material network (DMN) proposed by Liu et al. (2019) to tackle general 3-dimensional (3D) problems with arbitrary material and geometric nonlinearities. It discovers a new way of describing multiscale heterogeneous materials by a multi-layer network structure and mechanis...

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Veröffentlicht in:	Journal of the mechanics and physics of solids Jg. 127; S. 20 - 46
Hauptverfasser:	Liu, Zeliang, Wu, C.T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 01.06.2019 Elsevier BV
Schlagworte:	3D building-block Algorithms Carbon fiber reinforced plastics CFRP composites Computer simulation Crystal plasticity Elasticity Equilibrium conditions Exact solutions Extrapolation Fiber composites Fiber reinforced polymers Formulations Hyperelasticity Machine learning Mathematical models Mathematical morphology Model accuracy Multilayers Particulate composites Plastic properties Polymer matrix composites Rubber Three-scale homogenization 3D building-block Crystal plasticity CFRP composites Three-scale homogenization Machine learning Hyperelasticity
ISSN:	0022-5096, 1873-4782
Online-Zugang:	Volltext
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