A single Long Short-Term Memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy

TL;DR

This paper demonstrates that a single LSTM neural network can effectively predict complex path-dependent elastoplastic behaviors in heterogeneous and anisotropic materials, improving modeling accuracy over traditional methods.

Contribution

The study introduces a simple LSTM-based model capable of capturing elastoplastic responses considering material heterogeneity and anisotropy, advancing machine learning applications in material behavior prediction.

Findings

A single LSTM model accurately predicts J2 plasticity responses.

The model effectively captures path-dependent behaviors under various loading paths.

LSTM-based architecture can model responses of complex microstructures.

Abstract

This study presents the applicability of conventional deep recurrent neural networks (RNN) to predict path-dependent plasticity associated with material heterogeneity and anisotropy. Although the architecture of RNN possesses inductive biases toward information over time, it is still challenging to learn the path-dependent material behavior as a function of the loading path considering the change from elastic to elastoplastic regimes. Our attempt is to develop a simple machine-learning-based model that can replicate elastoplastic behaviors considering material heterogeneity and anisotropy. The basic Long-Short Term Memory Unit (LSTM) is adopted for the modeling of plasticity in the two-dimensional space by enhancing the inductive bias toward the past information through manipulating input variables. Our results find that a single LSTM based model can capture the J2 plasticity responses under both monotonic and arbitrary loading paths provided the material heterogeneity. The proposed neural network architecture is then used to model elastoplastic responses of a two-dimensional transversely anisotropic material associated with computational homogenization (FE2). It is also found that a single LSTM model can be used to accurately and effectively capture the path-dependent responses of heterogeneous and anisotropic microstructures under arbitrary mechanical loading conditions.