HOSSNet: An efficient physics-guided neural network for simulating micro-crack propagation
Abstract The Hybrid Optimization Software Suite (HOSS), which combines the finite-discrete
element method (FDEM), is an advanced approach for simulating high-fidelity fracture and …
element method (FDEM), is an advanced approach for simulating high-fidelity fracture and …
Accelerating high-strain continuum-scale brittle fracture simulations with machine learning
Failure in brittle materials under dynamic loading conditions is a result of the propagation
and coalescence of microcracks. Simulating this discrete crack evolution at the continuum …
and coalescence of microcracks. Simulating this discrete crack evolution at the continuum …
Graph neural networks for simulating crack coalescence and propagation in brittle materials
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-
based models can become computationally demanding as the number of microcracks …
based models can become computationally demanding as the number of microcracks …
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
Failure trajectories, probable failure zones, and damage indices are some of the key
quantities of relevance in brittle fracture mechanics. High-fidelity numerical solvers that …
quantities of relevance in brittle fracture mechanics. High-fidelity numerical solvers that …
Fracture pattern prediction with random microstructure using a physics-informed deep neural networks
Material fracture is a process involving both linear elastic stage and nonlinear crack
propagation stage. The problem becomes even complex when the material random …
propagation stage. The problem becomes even complex when the material random …
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling
Scale bridging is a critical need in computational sciences, where the modeling community
has developed accurate physics models from first principles, of processes at lower length …
has developed accurate physics models from first principles, of processes at lower length …
Statistically informed upscaling of damage evolution in brittle materials
The presence and growth of micro-cracks degrade the strength of brittle solids, greatly
impacting the overall material response. Hence, the evolution of these micro-cracks must be …
impacting the overall material response. Hence, the evolution of these micro-cracks must be …
Deep Learning for Multiscale Damage Analysis via Physics-Informed Recurrent Neural Network
S Deng - arXiv preprint arXiv:2212.01880, 2022 - arxiv.org
Direct numerical simulation of hierarchical materials via homogenization-based concurrent
multiscale models poses critical challenges for 3D large scale engineering applications, as …
multiscale models poses critical challenges for 3D large scale engineering applications, as …
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Despite their recent success, machine learning (ML) models such as graph neural networks
(GNNs), suffer from drawbacks such as the need for large training datasets and poor …
(GNNs), suffer from drawbacks such as the need for large training datasets and poor …
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
We propose a machine learning approach to address a key challenge in materials science:
predicting how fractures propagate in brittle materials under stress, and how these materials …
predicting how fractures propagate in brittle materials under stress, and how these materials …