Data-driven multiscale modelling of granular materials via knowledge transfer and sharing

Author:

Qu TongmingORCID,Zhao Jidong,Guan ShaohengORCID,Feng Y.T.ORCID

Funder

National Natural Science Foundation of China

Research Grants Council, University Grants Committee

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference53 articles.

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

2. Bahmani, B., Sun, W., 2021. Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings. arXiv preprint.

3. From CP-FFT to CP-RNN: recurrent Neural Network Surrogate Model Of Crystal Plasticity;Bonatti;Int. J. Plast.,2022

4. One for all: universal material model based on minimal state-space neural networks;Bonatti;Sci. Adv.,2021

5. On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids;Bonatti;J. Mech. Phys. Solids,2022

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