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

Recurrent neural networks and transfer learning for elasto-plasticity in woven composites