Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)

TL;DR

The paper introduces XDEM, a unified AI-driven framework that improves fracture mechanics modeling by integrating discrete and continuous approaches, leading to more accurate and efficient predictions with sparse data.

Contribution

XDEM combines discrete and phase-field fracture models into a single framework, overcoming limitations of existing DEM methods and enabling accurate predictions with sparse collocation points.

Findings

XDEM outperforms standard DEM in accuracy and efficiency.

It effectively models complex crack behaviors including kinked crack growth.

Validation on benchmark problems demonstrates robustness and versatility.

Abstract

Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation near cracks, face stability challenges, and typically treat discrete and continuous fracture models separately. To overcome these limitations, we introduce the Extended Deep Energy Method (XDEM), a unified deep learning framework that incorporates both displacement discontinuities and crack-tip asymptotics in the discrete setting, while flexibly coupling displacement and phase fields in the continuous setting. This integration enables accurate fracture predictions using uniformly distributed, relatively sparse collocation points. Validation across benchmark problems including stress intensity factor evaluation, straight and kinked crack growth, and complex crack initiation demonstrates that XDEM consistently outperforms standard DEM in accuracy and efficiency. By bridging discrete and phase-field models within a single framework, XDEM establishes a robust foundation for applying AI to fracture mechanics and opens new avenues for predictive modeling in engineering and materials science.