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Incompressible rubber thermoelasticity: a neural network approach - MaRDI portal

Incompressible rubber thermoelasticity: a neural network approach (Q6101617)

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scientific article; zbMATH DE number 7691230

Language	Label	Description	Also known as
English	Incompressible rubber thermoelasticity: a neural network approach	scientific article; zbMATH DE number 7691230

Statements

scholarly article

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Incompressible rubber thermoelasticity: a neural network approach (English)

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Marko Čanadija

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Computational Mechanics

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publication date

1 June 2023

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zbMATH Keywords

feed-forward neural network

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thermally expanded Ogden hyperelasticity

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Cauchy stress

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deep learning

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material stiffness matrix

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1007/s00466-023-02278-y

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

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Smart finite elements: a novel machine learning application

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Data-driven fracture mechanics

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Thermo-mechanics of rubberlike materials

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On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

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Entropy elasticity of isotropic rubber-like solids at finite strains

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Multilayer feedforward networks are universal approximators

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A machine learning based plasticity model using proper orthogonal decomposition

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A virtual element method for isotropic hyperelasticity

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Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties

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Deep learned finite elements

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Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

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A mixed shell formulation accounting for thickness strains and finite strain 3d material models

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

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

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Entropic thermoelasticity at finite strains. Aspects of the formulation and numerical implementation

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Large deformation isotropic elasticity – on the correlation of theory and experiment for incompressible rubberlike solids

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Large deformation isotropic elasticity: on the correlation of theory and experiment for compressible rubberlike solids

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Volume changes associated with the deformation of rubber-like solids

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Deep Belief Networks Are Compact Universal Approximators

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

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Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity

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Constrained neural network training and its application to hyperelastic material modeling

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Identifiers

zbMATH Open document ID

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10.1007/s00466-023-02278-y

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:6101617

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