MAP123-EP: a mechanistic-based data-driven approach for numerical elastoplastic analysis

DOI10.1016/j.cma.2020.112955zbMath1442.74044OpenAlexW3011127381MaRDI QIDQ2180473

Shan Tang, Sourav Saha, Hai Qiu, Ying Li, Satyajit Mojumder, Xu Guo, Hang Yang, Wing Kam Liu

Publication date: 14 May 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.112955

zbMATH Keywords

finite element analysisconstitutive lawelastoplastic materialdata-drivenstrain-driven

Mathematics Subject Classification ID

Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05) Finite element methods applied to problems in solid mechanics (74S05) PDEs in connection with mechanics of deformable solids (35Q74)

Related Items (9)

Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks ⋮ An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials ⋮ Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics ⋮ Modular machine learning-based elastoplasticity: generalization in the context of limited data ⋮ Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering ⋮ MAP123-EPF: a mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain ⋮ MAP123-EP ⋮ A data-driven approach for modeling tension-compression asymmetric material behavior: numerical simulation and experiment ⋮ A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

Cites Work

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