MAP123-EPF: a mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain

DOI10.1016/j.cma.2020.113484zbMath1506.74069OpenAlexW3096717971MaRDI QIDQ2020782

Shan Tang, Xu Guo, Hai Qiu, Hang Yang, Mark Fleming, Wing Kam Liu

Publication date: 26 April 2021

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.2020.113484

zbMATH Keywords

finite strainconstitutive modeldata-drivenelastoplastic materials

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) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30)

Related Items (4)

Machine learning based asymptotic homogenization and localization: Predictions of key local behaviors of multiscale configurations bearing microstructural varieties ⋮ Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics ⋮ Data-driven enhanced phase field models for highly accurate prediction of mode I and mode II fracture ⋮ A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

Uses Software

• ABAQUS
• WARP3D
• F3DAM
• MAP123-EP
• MAP123-EPF

Cites Work

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