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

Woven composites exhibit complex meso-scale behavior depending on meso- and micro-structural parameters. Accurately modeling their mechanical response is challenging and computationally demanding, especially for inelastic behavior. To address the computational burden, we have developed a Recurrent Neural Network (RNN) model as a surrogate for meso-scale simulations. As a basis for RNN training, a mean-field model generates a comprehensive data set representing elasto-plastic behavior. Arbitrary six-dimensional time histories of strain are used to generate multiaxial stress–strain histories under random walking and cyclic loading conditions as the source and target tasks, respectively. First, the RNN model is trained for the source task. The same model is trained leveraging transfer learning for the target task, containing fewer data and sparse features because only some strain components are non-zero. The candidate model is successfully trained and validated through a grid search exploration of over 220 different RNN configurations and demonstrates accurate predictions for both source and target tasks. The results demonstrate that transfer learning could be used to train the RNN effectively under varying strain conditions and arbitrary constituents' material properties, suggesting its potential as an appropriate tool for modeling path-dependent responses in woven composites.