A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Thermodynamics-based artificial neural networks for constitutive modeling
Abstract Machine Learning methods and, in particular, Artificial Neural Networks (ANNs)
have demonstrated promising capabilities in material constitutive modeling. One of the main …
have demonstrated promising capabilities in material constitutive modeling. One of the main …
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
NN Vlassis, WC Sun - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
We introduce a deep learning framework designed to train smoothed elastoplasticity models
with interpretable components, such as the stored elastic energy function, yield surface, and …
with interpretable components, such as the stored elastic energy function, yield surface, and …
Polyconvex anisotropic hyperelasticity with neural networks
In the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …
Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics
Analyzing and modeling the constitutive behavior of materials is a core area in materials
sciences and a prerequisite for conducting numerical simulations in which the material …
sciences and a prerequisite for conducting numerical simulations in which the material …
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 …
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 …
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …
with the prospect of significantly relieving the computational costs of hierarchical …
Learning constitutive relations using symmetric positive definite neural networks
We present a new neural-network architecture, called the Cholesky-factored symmetric
positive definite neural network (SPD-NN), for modeling constitutive relations in …
positive definite neural network (SPD-NN), for modeling constitutive relations in …
[HTML][HTML] Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling
Constitutive relation remains one of the most important, yet fundamental challenges in the
study of granular materials. Instead of using closed-form phenomenological models or …
study of granular materials. Instead of using closed-form phenomenological models or …