用几何和边界条件快速预测裂缝弹性变形，无需有限元数据。
Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry
- 用DeepONet结合几何编码，直接从形状和边界条件预测位移场。
- 弱化处理裂缝处的无应力边界条件，保持物理一致性。
- 无需训练数据，适合实时结构健康监测应用。
本文旨在为断裂弹性体的实时结构健康监测构建一种快速且物理一致的代理模型。提出一种物理信息引导的DeepONet框架，通过专门设计的编码策略处理裂纹几何，并在不依赖有限元生成训练数据的情况下，从边界条件与裂纹几何共同预测位移场。裂纹边界的无牵引条件通过局部惩罚项弱式施加。数值实验以单一典型裂纹几何为例，验证了方法可行性，为拓展至多种裂纹几何的代理建模奠定了基础。
原文摘要 · Abstract (English)
This work aims to develop a fast and physically consistent surrogate model for real-time structural health monitoring of fractured elastic domains. We propose a physics-informed DeepONet framework that predicts displacement fields from both boundary conditions and fracture geometry, using a dedicated encoding strategy for the latter and without relying on finite-element-generated training data. The traction-free condition on the fracture boundary is imposed weakly through a localized penalty term. The presented numerical example focuses on one representative fracture geometry, demonstrating the feasibility of the formulation and laying the groundwork for extensions to surrogate modeling across diverse fracture geometries.
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