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

Abueidda, 2021, Deep learning for plasticity and thermo-viscoplasticity, Int. J. Plast., 136, 10.1016/j.ijplas.2020.102852 Bahmani, B., Sun, W., 2021. Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings. arXiv preprint. Bonatti, 2022, From CP-FFT to CP-RNN: recurrent Neural Network Surrogate Model Of Crystal Plasticity, Int. J. Plast., 10.1016/j.ijplas.2022.103430 Bonatti, 2021, One for all: universal material model based on minimal state-space neural networks, Sci. Adv., 7, eabf3658, 10.1126/sciadv.abf3658 Bonatti, 2022, On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids, J. Mech. Phys. Solids, 158, 10.1016/j.jmps.2021.104697 Ellis, 1995, Stress-strain modeling of sands using artificial neural networks, J. Geotech. Eng., 121, 429, 10.1061/(ASCE)0733-9410(1995)121:5(429) Fazily, 2023, Machine learning-driven stress integration method for anisotropic plasticity in sheet metal forming, Int. J. Plast., 166, 10.1016/j.ijplas.2023.103642 Geers, 2010, Multi-scale computational homogenization: trends and challenges, J. Comput. Appl. Math., 234, 2175, 10.1016/j.cam.2009.08.077 Ghaboussi, 1991, Knowledge-based modeling of material behavior with neural networks, J. Eng. Mech., 117, 132, 10.1061/(ASCE)0733-9399(1991)117:1(132) Ghaboussi, 1998, New nested adaptive neural networks (NANN) for constitutive modeling, Comput. Geotech., 22, 29, 10.1016/S0266-352X(97)00034-7 Gorji, 2020, On the potential of recurrent neural networks for modeling path dependent plasticity, J. Mech. Phys. Solids, 143, 10.1016/j.jmps.2020.103972 Guan, 2023, Finite element geotechnical analysis incorporating deep learning-based soil model, Comput. Geotech., 154, 10.1016/j.compgeo.2022.105120 Guan, 2023, An explicit FEM-NN framework and the analysis of error caused by NN-predicted stress, Acta Geotech., 1 Guan, 2023, A machine learning-based multi-scale computational framework for granular materials, Acta Geotech., 18, 1699, 10.1007/s11440-022-01709-z Guo, 2014, A coupled FEM/DEM approach for hierarchical multiscale modelling of granular media, Int. J. Numer Methods Eng., 99, 789, 10.1002/nme.4702 Guo, 2016, Multiscale insights into classical geomechanics problems, Int. J. Numer. Anal. Methods Geomech., 40, 367, 10.1002/nag.2406 Guo, 2008, Elasto-plastic constitutive model for geotechnical materials with strain-softening behaviour, Comput. Geosci., 34, 14, 10.1016/j.cageo.2007.03.012 Heider, 2020, SO (3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials, Comput. Methods Appl. Mech. Eng., 363, 10.1016/j.cma.2020.112875 Huang, 2020, A machine learning based plasticity model using proper orthogonal decomposition, Comput. Methods Appl. Mech. Eng., 365, 10.1016/j.cma.2020.113008 Ibragimova, 2022, A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals, Int. J. Plast., 157, 10.1016/j.ijplas.2022.103374 Ibragimova, 2021, A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths, Int. J. Plast., 144, 10.1016/j.ijplas.2021.103059 Jang, 2021, Machine learning-based constitutive model for J2-plasticity, Int. J. Plast., 138, 10.1016/j.ijplas.2020.102919 Jordan, 2020, Neural network model describing the temperature-and rate-dependent stress-strain response of polypropylene, Int. J. Plast., 135, 10.1016/j.ijplas.2020.102811 Jung, 2006, Neural network constitutive model for rate-dependent materials, Comput. Struct., 84, 955, 10.1016/j.compstruc.2006.02.015 Karapiperis, 2021, Data-driven multiscale modeling in mechanics, J. Mech. Phys. Solids, 147, 104239, 10.1016/j.jmps.2020.104239 Kawamoto, 2018, All you need is shape: predicting shear banding in sand with LS-DEM, J. Mech. Phys. Solids, 111, 375, 10.1016/j.jmps.2017.10.003 Kuhn, 2018, Multi-directional behavior of granular materials and its relation to incremental elasto-plasticity, Int. J. Solids Struct., 152, 305, 10.1016/j.ijsolstr.2018.07.005 Li, 2019, Machine-learning based temperature-and rate-dependent plasticity model: application to analysis of fracture experiments on DP steel, Int. J. Plast., 118, 320, 10.1016/j.ijplas.2019.02.012 Liang, 2019, Multiscale modeling of large deformation in geomechanics, Int. J. Numer. Anal. Methods Geomech., 43, 1080, 10.1002/nag.2921 Liu, 2021, Knowledge extraction and transfer in data-driven fracture mechanics, Proc. Natl. Acad. Sci., 118 Ma, 2022, A predictive deep learning framework for path-dependent mechanical behavior of granular materials, Acta Geotech., 17, 3463, 10.1007/s11440-021-01419-y Masi, 2022, Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN), Comput. Methods Appl. Mech. Eng., 398, 10.1016/j.cma.2022.115190 Masi, 2021, Thermodynamics-based Artificial Neural Networks for constitutive modeling, J. Mech. Phys. Solids, 147, 10.1016/j.jmps.2020.104277 Mozaffar, 2019, Deep learning predicts path-dependent plasticity, Proc. Natl. Acad. Sci., 116, 26414, 10.1073/pnas.1911815116 Nascimento, 2023, A machine learning model to predict yield surfaces from crystal plasticity simulations, Int. J. Plast., 161, 10.1016/j.ijplas.2022.103507 Perera, 2022, A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks, Mech. Mater., 181, 104639 Pouragha, 2017, Non-dissipative structural evolutions in granular materials within the small strain range, Int. J. Solids Struct., 110, 94, 10.1016/j.ijsolstr.2017.01.039 Qu, 2021, Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning, Int. J. Plast., 144, 10.1016/j.ijplas.2021.103046 Qu, 2021, Deep learning predicts stress–strain relations of granular materials based on triaxial testing data, Comput. Model. Eng. Sci., 128, 129 Qu, 2019, Discrete element modelling of flexible membrane boundaries for triaxial tests, Comput. Geotech., 115, 10.1016/j.compgeo.2019.103154 Qu, 2023, Deep active learning for constitutive modelling of granular materials: from representative volume elements to implicit finite element modelling, Int. J. Plast., 164, 10.1016/j.ijplas.2023.103576 Su, 2023, A multifidelity neural network (MFNN) for constitutive modeling of complex soil behaviors, Int. J. Numer. Anal. Methods Geomech, 10.1002/nag.3620 Tancogne-Dejean, 2021, Recurrent neural network modeling of the large deformation of lithium-ion battery cells, Int. J. Plast., 146, 10.1016/j.ijplas.2021.103072 Vlassis, 2021, Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening, Comput. Methods Appl. Mech. Eng., 377, 10.1016/j.cma.2021.113695 Wang, 2021, Predicting fault slip via transfer learning, Nat. Commun., 12, 7319, 10.1038/s41467-021-27553-5 Wang, 2019, Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning, Comput. Methods Appl. Mech. Eng., 346, 216, 10.1016/j.cma.2018.11.026 Wang, 2019, A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation, Comput. Mech., 64, 467, 10.1007/s00466-019-01723-1 Wang, 2022, Data-driven strain–stress modelling of granular materials via temporal convolution neural network, Comput. Geotech., 152, 10.1016/j.compgeo.2022.105049 Wen, 2021, Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation, J. Mech. Phys. Solids, 153, 10.1016/j.jmps.2021.104481 Wu, 2022, Constitutive modelling of natural sands using a deep learning approach accounting for particle shape effects, Powder Technol., 404, 10.1016/j.powtec.2022.117439 Zhang, 2020, Using neural networks to represent von Mises plasticity with isotropic hardening, Int. J. Plast., 132, 10.1016/j.ijplas.2020.102732 Zhang, 2022, Physics-constrained hierarchical data-driven modelling framework for complex path-dependent behaviour of soils, Int. J. Numer. Anal. Methods Geomech., 46, 1831, 10.1002/nag.3370 Zhang, 2020, An AI-based model for describing cyclic characteristics of granular materials, Int. J. Numer. Anal. Methods Geomech., 44, 1315, 10.1002/nag.3063