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.

本研究介绍了循环神经网络（RNN）模型作为计算密集型中尺度模拟编织复合材料的替代方法。通过利用迁移学习的力量，解决了 RNN 模型中初始化挑战和稀疏数据问题。一个平均场模型产生了代表弹塑性行为的全面数据集。仿真中，使用任意六维应变历史来预测随机行走作为源任务和循环加载条件作为目标任务下的应力。整合子尺度特性增强了 RNN 的多功能性。为了实现准确预测，该模型使用网格搜索方法来调整网络结构和超参数配置。本研究结果表明，迁移学习可用于有效适应不同应变条件的 RNN，从而为编织复合材料中的路径相关响应建模提供了有用工具。