标题： 强非线性粘弹性软材料中的应力软化损伤 带有时温耦合的物理信息数据驱动本构模型 显示英文标题

标题： Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling

摘要： 本研究提出了一种新颖的物理信息数据驱动建模框架，用于捕捉表现出应力软化的软材料的强非线性热粘弹性行为，重点在于穆林斯效应。 与以往仅限于准静态或等温条件的方法不同，我们的模型统一了速率依赖性、温度敏感性、大应变循环加载和演变损伤机制。 通过自定义损失函数确保热力学可行性，该函数嵌入了克劳修斯-杜赫姆不等式，并显式约束损伤变量以实现物理上现实的软化。 时间卷积网络在多个温度、应变率和拉伸水平的高保真实验数据上进行训练，使模型能够捕捉丰富的热机械耦合和历史依赖性。 该框架可推广到未见过的热机械条件、更高的应变率和更大的变形，并对输入噪声具有鲁棒性。 与使用Abaqus/Explicit的有限元模拟对比验证表明，在循环加载和损伤演化下具有良好的一致性，证实了代理模型在高级仿真工作流中的有效性。 显示英文摘要

摘要： This study presents a novel physics informed, data-driven modeling framework for capturing the strongly nonlinear thermo-viscoelastic behavior of soft materials exhibiting stress softening, with emphasis on the Mullins effect. Unlike previous approaches limited to quasi-static or isothermal conditions, our model unifies rate dependence, temperature sensitivity, large strain cyclic loading, and evolving damage mechanisms. Thermodynamic admissibility is ensured via a custom loss function that embeds the Clausius Duhem inequality and explicitly constrains the damage variable for physically realistic softening. A Temporal Convolutional Network is trained on high fidelity experimental data across multiple temperatures, strain rates, and stretch levels, enabling the model to capture rich thermomechanical coupling and history dependence. The framework generalizes to unseen thermo mechanical conditions, higher strain rates, and larger deformations, and remains robust to input noise. Validation against finite element simulations using Abaqus/Explicit demonstrates excellent agreement under cyclic loading and damage evolution, confirming the surrogate models effectiveness for advanced simulation workflows.