Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning-Reference-Cited by-同舟云学术

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Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

Published:2021-09 Issue: Volume:144 Page:103046
ISSN:0749-6419
Container-title:International Journal of Plasticity
language:en
Short-container-title:International Journal of Plasticity

Author:

Qu Tongming,Di Shaocheng,Feng Y.T.,Wang Min,Zhao Tingting

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference67 articles.

1. Deep learning for plasticity and thermo-viscoplasticity;Abueidda;Int. J. Plast.,2021

2. Application of artificial neural networks in micromechanics for polycrystalline metals;Ali;Int. J. Plast.,2019

3. Granular materials: constitutive equations and strain localization;Anand;J. Mech. Phys. Solids,2000

4. Multi-mechanism anisotropic model for granular materials;Anandarajah;Int. J. Plast.,2008

5. Influence of particle shape on granular contact signatures and shear strength: new insights from simulations;Antony;Int. J. Solids Struct.,2004

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