Multiscale Analysis of Woven Composites Using Hierarchical Physically Recurrent Neural Networks
For engineers modeling woven composites, this provides a computationally efficient and explainable surrogate that reduces data requirements and improves extrapolation over existing neural network approaches.
This study introduces a Hierarchical Physically Recurrent Neural Network (HPRNN) for multiscale homogenization of woven composites, achieving better generalization under complex cyclic loading while avoiding nonphysical predictions common in pure data-driven models.
Multiscale homogenization of woven composites requires detailed micromechanical evaluations, leading to high computational costs. Data-driven surrogate models based on neural networks address this challenge but often suffer from big data requirements, limited interpretability, and poor extrapolation capabilities. This study introduces a Hierarchical Physically Recurrent Neural Network (HPRNN) employing two levels of surrogate modeling. First, Physically Recurrent Neural Networks (PRNNs) are trained to capture the nonlinear elasto-plastic behavior of warp and weft yarns using micromechanical data. In a second scale transition, a physics-encoded meso-to-macroscale model integrates these yarn surrogates with the matrix constitutive model, embedding physical properties directly into the latent space. Adopting HPRNNs for both scale transitions can avoid nonphysical behavior often observed in predictions from pure data-driven recurrent neural networks and transformer networks. This results in better generalization under complex cyclic loading conditions. The framework offers a computationally efficient and explainable solution for multiscale modeling of woven composites.