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

Ji-hao Qi; Xin Chen; Zhi-wei Pang; Zeng Lin; Chao-yuan Wang; Hu Liu; Jin Sha; Zhi-shan Bai

doi:10.11777/j.issn1000-3304.2025.25155

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

Review | 更新时间：2025-10-29

- Physics-Informed Deep Learning for Polymer Rheology: Investigating Challenges, Methodologies, and Applications
- Acta Polymerica Sinica Vol. 56, Issue 11, Pages: 1910-1925(2025)
- 作者机构：
  
  华东理工大学机械与动力工程学院上海 200237
- 作者简介：
- 基金信息：
- DOI：10.11777/j.issn1000-3304.2025.25155
  CLC：
- CSTR：32057.14.GFZXB.2025.7457
- Received：26 June 2025，
  
  Accepted：15 August 2025，
  
  Published Online：26 September 2025，
  
  Published：20 November 2025
- 稿件说明：
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祁纪浩, 陈欣, 庞志威, 林增, 王超元, 刘虎, 沙金, 白志山. 基于物理信息深度学习的高分子流变学研究：挑战、方法与应用. 高分子学报, 2025, 56(11), 1910-1925

Qi J. H.; Chen X.; Pang Z. W.; Lin Z.; Wang C. Y.; Liu H.; Sha J.; Bai Z. S. Physics-informed deep learning for polymer rheology: investigating challenges, methodologies, and applications. Acta Polymerica Sinica, 2025, 56(11), 1910-1925
祁纪浩, 陈欣, 庞志威, 林增, 王超元, 刘虎, 沙金, 白志山. 基于物理信息深度学习的高分子流变学研究：挑战、方法与应用. 高分子学报, 2025, 56(11), 1910-1925 DOI： 10.11777/j.issn1000-3304.2025.25155. CSTR： 32057.14.GFZXB.2025.7457.

Qi J. H.; Chen X.; Pang Z. W.; Lin Z.; Wang C. Y.; Liu H.; Sha J.; Bai Z. S. Physics-informed deep learning for polymer rheology: investigating challenges, methodologies, and applications. Acta Polymerica Sinica, 2025, 56(11), 1910-1925 DOI： 10.11777/j.issn1000-3304.2025.25155. CSTR： 32057.14.GFZXB.2025.7457.

摘要

高分子流变学旨在理解材料从微观到宏观的流动与变形特性，但传统建模方法在处理复杂非线性、多尺度模拟及数据解析方面长期面临挑战. 物理信息深度学习(PIDL)通过将流变学物理定律嵌入深度神经网络，减少对大量数据的依赖，提高模型泛化能力和物理合理性，为解决高分子流变学面临的挑战提供新的途径. 通过探讨PIDL的基本原理，包括其模型架构、物理信息损失函数设计(如MSE、MAE及稀疏/残差损失)和损失函数优化方法(如梯度下降、自适应权重调整)，并分析PIDL在高分子流变学中的具体应用，涵盖了概率模型、生成式模型、神经算子、图神经网络和强化学习等多种模型架构，展示其在参数优化、流变特性预测、逆问题求解及数字孪生集成等方面的潜力. 尽管PIDL展现出显著优势，但仍面临数据获取成本高、模型可解释性弱、多尺度求解精度不足及鲁棒性等问题. 未来发展方向包括利用多保真技术、物理信息增强数据、优化算法、结合数字孪生、推断因果关系以及应用元学习和可解释人工智能(XAI)来提升模型性能和实用性.

Abstract

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.

关键词

Keywords

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