Accurate cyclic plastic analysis using a neural network material model

DOI10.1016/S0955-7997(03)00050-XzbMath1106.74353MaRDI QIDQ597632

Sumit K. Garg

Publication date: 6 August 2004

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

zbMATH Keywords

Inverse problemFinite element analysisCyclic plastic analysisMaterial modelingNeural network material model

Mathematics Subject Classification ID

Related Items (9)

Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections ⋮ Optimal parameters selection of back propagation algorithm in the feedforward neural network ⋮ A framework for neural network based constitutive modelling of inelastic materials ⋮ Stochastic identification of elastic constants for anisotropic materials ⋮ A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths ⋮ Multiscale topology optimization using neural network surrogate models ⋮ Extracting inelastic metal behaviour through inverse analysis: a shift in focus from material models to material behaviour ⋮ Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel ⋮ Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step

Cites Work

• Constitutive equations for cyclic plasticity and cyclic viscoplasticity
• A non-linear uniaxial integral constitutive equation incorporating rate effects. Creep and relaxation
• An automated system for simulation and parameter identification of inelastic constitutive models
• Plastic stress-strain matrix and its application for the solution of elastic-plastic problems by the finite element method
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