Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry

• URL: http://arxiv.org/abs/2607.09382v1
• Date: Fri, 10 Jul 2026 13:04:08 GMT
• Title: Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry
• Abstract summary: This work aims to develop a fast and physically consistent surrogate model for real-time structural health monitoring of fractured elastic domains.<n>We propose a physics-informed DeepONet framework that predicts displacement fields from both boundary conditions and fracture geometry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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