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

TL;DR

This paper introduces a variational deep learning framework using higher-order B-spline basis functions for phase-field modelling of brittle fracture in anisotropic media, capturing direction-dependent crack growth.

Contribution

It presents the first variational deep learning approach for higher-order anisotropic phase-field fracture models, avoiding automatic differentiation and accurately modeling anisotropic crack propagation.

Findings

Successfully models direction-dependent crack growth in anisotropic media.

Enriches trial space with higher-order B-splines for stable gradient representation.

Demonstrates effectiveness on isotropic, cubic, and orthotropic fracture energies.

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.