[HTML][HTML] A review of peridynamic theory and nonlocal operators along with their computer implementations
This study presents a comprehensive exploration of Peridynamic (PD) theory, with a specific
focus on its theoretical foundations and practical implementations, including various PD …
focus on its theoretical foundations and practical implementations, including various PD …
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 …
Physics-informed deep neural operator networks
Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
A comprehensive review on the vibration analyses of small-scaled plate-based structures by utilizing the nonclassical continuum elasticity theories
In recent years, mechanical characteristics including vibration, bending, buckling and
postbuckling, stability and instability, etc. of small-scaled structures (such as …
postbuckling, stability and instability, etc. of small-scaled structures (such as …
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
Multiscale modeling is an effective approach for investigating multiphysics systems with
largely disparate size features, where models with different resolutions or heterogeneous …
largely disparate size features, where models with different resolutions or heterogeneous …
Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling
Constitutive modeling based on continuum mechanics theory has been a classical approach
for modeling the mechanical responses of materials. However, when constitutive laws are …
for modeling the mechanical responses of materials. However, when constitutive laws are …
Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses
Neural operators, which can act as implicit solution operators of hidden governing
equations, have recently become popular tools for learning the responses of complex real …
equations, have recently become popular tools for learning the responses of complex real …
A neural network peridynamic method for modeling rubber-like materials
Y Chen, Y Yang, Y Liu - International Journal of Mechanical Sciences, 2024 - Elsevier
Peridynamic (PD) is a powerful tool for simulating the large deformation and failure process
of many types of materials. However, its use in modeling rubber-like materials is limited due …
of many types of materials. However, its use in modeling rubber-like materials is limited due …
Prediction of graphene's mechanical and fracture properties via peridynamics
Although graphene is believed to be the strongest material, many properties of this material
are still worth exploring and discovering, especially the influence of inevitable defects in its …
are still worth exploring and discovering, especially the influence of inevitable defects in its …
Machine learning of nonlocal micro-structural defect evolutions in crystalline materials
The presence and evolution of defects that appear in the manufacturing process play a vital
role in the failure mechanisms of engineering materials. In particular, the collective behavior …
role in the failure mechanisms of engineering materials. In particular, the collective behavior …