MAP123: a data-driven approach to use 1D data for 3D nonlinear elastic materials modeling

DOI10.1016/j.cma.2019.112587zbMath1442.65417OpenAlexW2969418368WikidataQ127355040 ScholiaQ127355040MaRDI QIDQ2179201

Ying Li, Shan Tang, Gang Zhang, Xu Guo, Hang Yang, Wing Kam Liu

Publication date: 12 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.2019.112587

zbMATH Keywords

finite element analysisdata-driven approachmaterial lawcompressed data

Mathematics Subject Classification ID

Nonlinear elasticity (74B20) Boundary element methods for boundary value problems involving PDEs (65N38)

Related Items

Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks ⋮ MAP123-EP: a mechanistic-based data-driven approach for numerical elastoplastic analysis ⋮ A machine learning based plasticity model using proper orthogonal decomposition ⋮ 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 ⋮ Frankenstein's data-driven computing approach to model-free mechanics ⋮ Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity ⋮ 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 ⋮ Adaptive selection of reference stiffness in virtual clustering analysis ⋮ A kernel method for learning constitutive relation in data-driven computational elasticity ⋮ Data-driven enhanced phase field models for highly accurate prediction of mode I and mode II fracture ⋮ A full field solution for an anisotropic elastic plate with a hole perturbed from an ellipse ⋮ A data-driven approach for modeling tension-compression asymmetric material behavior: numerical simulation and experiment

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials
• Data-driven problems in elasticity
• Rate effects on toughness in elastic nonlinear viscous solids
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• Data driven computing with noisy material data sets
• Computational mechanics enhanced by deep learning
• Data-based derivation of material response
• A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
• Data-driven computational mechanics
• Constitutive analysis of elastic-plastic crystals at arbitrary strain
• Large deformation isotropic elasticity – on the correlation of theory and experiment for incompressible rubberlike solids