A comparative study on different neural network architectures to model inelasticity (Q6082629)

scientific article; zbMATH DE number 7772333

Language	Label	Description	Also known as
English	A comparative study on different neural network architectures to model inelasticity	scientific article; zbMATH DE number 7772333

Statements

instance of

scholarly article

0 references

title

A comparative study on different neural network architectures to model inelasticity (English)

0 references

0 references

0 references

0 references

0 references

International Journal for Numerical Methods in Engineering

0 references

publication date

30 November 2023

0 references

full work available at URL

https://arxiv.org/abs/2303.03402

0 references

zbMATH Keywords

constitutive modeling

0 references

enforcing physics

0 references

neural networks

0 references

plasticity

0 references

recurrent neural networks

0 references

thermodynamic consistency

0 references

viscoelasticity

0 references

MaRDI profile type

MaRDI publication profile

0 references

cites work

Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

0 references

Physics informed neural networks for continuum micromechanics

0 references

A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

0 references

Finite electro-elasticity with physics-augmented neural networks

0 references

\(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

0 references

Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning

0 references

Data-driven tissue mechanics with polyconvex neural ordinary differential equations

0 references

Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity

0 references

Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints

0 references

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

0 references

Q4253789

0 references

Numerical implementation of a neural network based material model in finite element analysis

0 references

Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network

0 references

Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network

0 references

A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

0 references

\(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

0 references

A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials

0 references

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

0 references

On the micromechanics of deep material networks

0 references

An FE-DMN method for the multiscale analysis of thermomechanical composites

0 references

Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening

0 references

Modular machine learning-based elastoplasticity: generalization in the context of limited data

0 references

Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN)

0 references

Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials

0 references

Strain-driven homogenization of inelastic microstructures and composites based on an incremental variational formulation

0 references

Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations

0 references

Q4935947

0 references

Homogenization of inelastic solid materials at finite strains based on incremental minimization principles. Application to the texture analysis of polycrystals.

0 references

Computational intelligence. A methodological introduction. With contributions from Frank Klawonn and Christian Moewes

0 references

Deep Learning in Computational Mechanics

0 references

Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials

0 references

Data-driven elasto-(visco)-plasticity involving hidden state variables

0 references

Identifiers

arXiv ID

2303.03402

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6082629