  As a surrogate for computationally intensive meso-scale simulation of woven
composites, this article presents Recurrent Neural Network (RNN) models.
Leveraging the power of transfer learning, the initialization challenges and
sparse data issues inherent in cyclic shear strain loads are addressed in the
RNN models. A mean-field model generates a comprehensive data set representing
elasto-plastic behavior. In simulations, arbitrary six-dimensional strain
histories are used to predict stresses under random walking as the source task
and cyclic loading conditions as the target task. Incorporating sub-scale
properties enhances RNN versatility. In order to achieve accurate predictions,
the model uses a grid search method to tune network architecture and
hyper-parameter configurations. The results of this study demonstrate that
transfer learning can be used to effectively adapt the RNN to varying strain
conditions, which establishes its potential as a useful tool for modeling
path-dependent responses in woven composites.
