Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity
We present a machine learning approach that integrates geometric deep learning and
Sobolev training to generate a family of finite strain anisotropic hyperelastic models that …
Sobolev training to generate a family of finite strain anisotropic hyperelastic models that …
Theoretical overview of hydraulic fracturing break-down pressure
The precise prediction of the break-down pressure is imperative to define the pumping
schedule and the relevant stimulation parameters of a hydraulic fracturing process. A …
schedule and the relevant stimulation parameters of a hydraulic fracturing process. A …
[HTML][HTML] A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture
Numerical modeling of localizations is a challenging task due to the evolving rough solution
in which the localization paths are not predefined. Despite decades of efforts, there is a need …
in which the localization paths are not predefined. Despite decades of efforts, there is a need …
RKPM2D: an open-source implementation of nodally integrated reproducing kernel particle method for solving partial differential equations
We present an open-source software RKPM2D for solving PDEs under the reproducing
kernel particle method (RKPM)-based meshfree computational framework. Compared to …
kernel particle method (RKPM)-based meshfree computational framework. Compared to …
A naturally stabilized semi-Lagrangian meshfree formulation for multiphase porous media with application to landslide modeling
A stabilized meshfree formulation for modeling nonlinear, multiphase porous media with
application to landslide simulation is presented. To effectively capture the hydromechanical …
application to landslide simulation is presented. To effectively capture the hydromechanical …
Peridynamic simulation on fracture mechanical behavior of granite containing a single fissure after thermal cycling treatment
In this study, the thermal-mechanical fracturing behaviors of granite after thermal cycling
treatment are investigated using the fully coupled ordinary state-based peridynamic method …
treatment are investigated using the fully coupled ordinary state-based peridynamic method …
A neural network‐enhanced reproducing kernel particle method for modeling strain localization
Modeling the localized intensive deformation in a damaged solid requires highly refined
discretization for accurate prediction, which significantly increases the computational cost …
discretization for accurate prediction, which significantly increases the computational cost …
A deformation-dependent coupled Lagrangian/semi-Lagrangian meshfree hydromechanical formulation for landslide modeling
J Baek, RT Schlinkman, FN Beckwith… - Advanced Modeling and …, 2022 - Springer
The numerical modelling of natural disasters such as landslides presents several
challenges for conventional mesh-based methods such as the finite element method (FEM) …
challenges for conventional mesh-based methods such as the finite element method (FEM) …
A hyper-reduction computational method for accelerated modeling of thermal cycling-induced plastic deformations
For materials under cyclic thermal loadings, temperature and strain rate-dependent creep
deformation can occur due to the thermal expansion mismatch near material interfaces …
deformation can occur due to the thermal expansion mismatch near material interfaces …
A Lagrangian/semi-Lagrangian coupling approach for accelerated meshfree modelling of extreme deformation problems
In the reproducing kernel particle method (RKPM), the approximation is achieved through
construction of shape functions in the physical domain and the interaction of neighbouring …
construction of shape functions in the physical domain and the interaction of neighbouring …