Feed-Forward Neural Networks for Failure Mechanics Problems

This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks-in particular, the feed-forward neural network (FFNN)-are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.

Automated summary

This study explores an efficient neural network representation for isotropic brittle fracture phase-field modeling, using deep neural networks like FFNN. The trained models are verified and generalized through numerical examples, showing promising results close to the exact solutions.