Deep learning for property prediction of natural fiber polymer composites

Abstract The increasing availability of diverse experimental and computational data has accelerated the application of deep learning (DL) techniques for predicting polymer properties. A literature review was conducted to show recent advances in DL applied to this field. For example, Li et al. (2023) achieved an \(R^2>0.96\) for predicting stiffness tensors of carbon fiber composites using a hybrid CNN–MLP model trained on microstructure images and two-point statistics. Aligning with this approach, Xue et al. (2023) compared DNN performance with genetic