A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
Many geological materials, such as shale, mudstone, carbonate rock, limestone and rock
salt are multi-porosity porous media in which pores of different scales may co-exist in the …
salt are multi-porosity porous media in which pores of different scales may co-exist in the …
Coupled phase-field and plasticity modeling of geological materials: From brittle fracture to ductile flow
The failure behavior of geological materials depends heavily on confining pressure and
strain rate. Under a relatively low confining pressure, these materials tend to fail by brittle …
strain rate. Under a relatively low confining pressure, these materials tend to fail by brittle …
Smart finite elements: A novel machine learning application
Many multiscale finite element formulations can become computationally expensive
because they rely on detailed models of the element's internal displacement field. This issue …
because they rely on detailed models of the element's internal displacement field. This issue …
Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
NN Vlassis, WC Sun - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
The history-dependent behaviors of classical plasticity models are often driven by internal
variables evolved according to phenomenological laws. The difficulty to interpret how these …
variables evolved according to phenomenological laws. The difficulty to interpret how these …
SO (3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
This paper examines the frame-invariance (and the lack thereof) exhibited in simulated
anisotropic elasto-plastic responses generated from supervised machine learning of …
anisotropic elasto-plastic responses generated from supervised machine learning of …
DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions
This contribution presents a meta-modeling framework that employs artificial intelligence to
design a neural network that replicates the path-dependent constitutive responses of …
design a neural network that replicates the path-dependent constitutive responses of …
Investigating mesh sensitivity and polycrystalline RVEs in crystal plasticity finite element simulations
H Lim, CC Battaile, JE Bishop, JW Foulk III - International Journal of …, 2019 - Elsevier
Crystal plasticity-finite element method (CP-FEM) is now widely used to understand the
mechanical response of polycrystalline materials. However, quantitative mesh convergence …
mechanical response of polycrystalline materials. However, quantitative mesh convergence …
A stabilized assumed deformation gradient finite element formulation for strongly coupled poromechanical simulations at finite strain
An adaptively stabilized finite element scheme is proposed for a strongly coupled hydro‐
mechanical problem in fluid‐infiltrating porous solids at finite strain. We first present the …
mechanical problem in fluid‐infiltrating porous solids at finite strain. We first present the …
Stress representations for tensor basis neural networks: alternative formulations to Finger–Rivlin–Ericksen
Data-driven constitutive modeling frameworks based on neural networks and classical
representation theorems have recently gained considerable attention due to their ability to …
representation theorems have recently gained considerable attention due to their ability to …
Computational thermomechanics of crystalline rock, Part I: A combined multi-phase-field/crystal plasticity approach for single crystal simulations
Rock salt is one of the major materials used for nuclear waste geological disposal. The
desired characteristics of rock salt, ie, high thermal conductivity, low permeability, and self …
desired characteristics of rock salt, ie, high thermal conductivity, low permeability, and self …