{"slug": "a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for", "title": "A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling", "summary": "Researchers proposed a hybrid GNN-FEM framework for phase-field fracture simulation that integrates a graph neural network surrogate into the conventional staggered scheme, replacing the phase-field update while retaining the FEM-based displacement solver. The approach achieves strong generalization across varying geometries, loading conditions, and material properties, reducing computational cost while maintaining accuracy compared to conventional FEM.", "body_md": "arXiv:2606.19378v1 Announce Type: new\nAbstract: Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge. In this study, we propose a hybrid GNN--FEM framework for efficient and generalizable phase-field fracture modeling. While phase-field approaches provide a robust variational framework for simulating complex crack evolution, their high computational cost limits practical applications because they require solving coupled, nonlinear, and history-dependent systems within an incremental finite element procedure. To address this challenge, a graph neural network surrogate is integrated into the conventional staggered scheme, replacing the phase-field update at each load increment while retaining the FEM-based displacement solver to enforce mechanical equilibrium and boundary conditions. By preserving the incremental solution structure, the framework remains consistent with history-dependent fracture evolution without requiring the surrogate to approximate the full solution trajectory. This selective surrogate strategy emphasizes the identification of a physically meaningful and incrementally structured learning target, rather than relying on brute-force data generation to learn the full fracture process. The proposed framework achieves strong generalization across varying geometries, loading conditions, material properties, and discretizations through dimensionless feature design, a graph-based formulation on mesh-based domains, and a physics-informed loss derived from the governing phase-field equation. Numerical experiments demonstrate that the hybrid approach reduces computational cost while maintaining accuracy compared with conventional FEM, and exhibits robust predictive performance across diverse problem settings.", "url": "https://wpnews.pro/news/a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for", "canonical_source": "https://arxiv.org/abs/2606.19378", "published_at": "2026-06-19 04:00:00+00:00", "updated_at": "2026-06-19 04:09:03.428914+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for", "markdown": "https://wpnews.pro/news/a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for.md", "text": "https://wpnews.pro/news/a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for.txt", "jsonld": "https://wpnews.pro/news/a-hybrid-gnn-fem-framework-for-phase-field-fracture-simulation-physics-for.jsonld"}}