文献类型：期刊文献

英文题名：Physics-Informed Deep Learning for Polymer Rheology: Investigating Challenges, Methodologies, and Applications

作者：Qi, Ji-hao[1];Chen, Xin[1];Pang, Zhi-wei[1];Lin, Zeng[1];Wang, Chao-yuan[1];Liu, Hu[1];Sha, Jin[1];Bai, Zhi-shan[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：56

期号：11

起止页码：1910

外文期刊名：ACTA POLYMERICA SINICA

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001629497000004)】；

语种：英文

外文关键词：Physics-informed deep learning; Polymer rheology; Modeling and simulation; Intelligent prediction and optimization

摘要：Polymer rheology aims to understand the multiscale flow and deformation of macromolecular materials, yet conventional constitutive approaches have long struggled with pronounced nonlinearities, cross-scale coupling and sparse, noisy data inversion. Physics-informed deep learning (PIDL) embeds rheological conservation laws and constitutive relations directly into deep neural networks, substantially reducing the demand for large labelled datasets while enhancing model generalization and physical interpretability. This review systematically outlines the foundational principles of PIDL, covering network architectures, physics-informed loss functions such as MSE, MAE, sparse and residual variants, and optimization strategies including gradient descent and adaptive weighting. We further survey five PIDL paradigms-probabilistic models, generative models, neural operators, graph neural networks and reinforcement learning-and demonstrate their potential for parameter identification, rheological property prediction, inverse problem solving and digital-twin integration. Despite these advantages, PIDL still contends with high data acquisition costs, limited interpretability, insufficient accuracy across scales and uncertain robustness under extreme conditions. Future research should exploit multi-fidelity techniques, physics-augmented data generation, advanced optimization, causal inference and explainable AI to deliver trustworthy, industrially viable rheological models.