Deep learning-based phase-field modelling of brittle fracture in anisotropic media

N. Plungė; P. Brommer; R. S. Edwards; E. G. Kakouris

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arXiv · 2603.20120

Deep learning-based phase-field modelling of brittle fracture in anisotropic media

N. Plungė·P. Brommer·R. S. Edwards·E. G. Kakouris

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Abstract

This work presents a variational physics-informed deep learning framework for phase-field modelling of brittle crack propagation in anisotropic media. Previous Deep Ritz Method (DRM) approaches have focused on second-order, isotropic phase-field fracture formulations. In contrast, the present work introduces, for the first time within a variational deep learning setting, a family of higher-order anisotropic phase-field models through a generalised crack density functional. The resulting fracture problem is solved by minimising the total energy using the DRM. The trial space is enriched with higher-order B-spline basis functions to represent higher-order gradients accurately and stably, thereby eliminating the need for conventional automatic differentiation. The methodology is assessed for isotropic, cubic, and orthotropic fracture surface energy densities. Numerical examples demonstrate direction-dependent crack growth in anisotropic cases, highlighting the capability of the method to accurately capture this behaviour.

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N. Plungė, P. Brommer, R. S. Edwards, E. G. Kakouris. 2026-03-20. Deep learning-based phase-field modelling of brittle fracture in anisotropic media. https://arxiv.org/abs/2603.20120

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