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NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks - MaRDI portal

NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks (Q6499924)

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scientific article; zbMATH DE number 7845211

Language	Label	Description	Also known as
English	NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks	scientific article; zbMATH DE number 7845211

Statements

scholarly article

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NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks (English)

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Emmanuel Baranger

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Ludovic Chamoin

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International Journal for Numerical Methods in Engineering

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publication date

10 May 2024

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zbMATH Keywords

constitutive modeling

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constitutive relation error

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physics-informed neural network

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unsupervised learning

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MaRDI profile type

MaRDI publication profile

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Inverse problems in elasticity

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A finite element formulation to identify damage fields: the equilibrium gap method

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Special virtual fields for the direct determination of material parameters with the virtual field method. I: Principle and definition

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Reciprocity principle and crack identification

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Data-driven computational mechanics

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Application of <i>a posteriori</i> error estimation for structural model updating

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Large scale parameter estimation problems in frequency-domain elastodynamics using an error in constitutive equation functional

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Model validation in the presence of uncertain experimental data

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Three-dimensional transient elastodynamic inversion using an error in constitutive relation functional

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Parameter identification and model updating in the context of nonlinear mechanical behaviors using a unified formulation of the modified constitutive relation error concept

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mCRE-based parameter identification from full-field measurements: consistent framework, integrated version, and extension to nonlinear material behaviors

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Identification strategy in the presence of corrupted measurements

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Modified constitutive relation error: an identification framework dealing with the reliability of information

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Multilayer feedforward networks are universal approximators

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

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

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

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

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

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

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Learning constitutive relations from indirect observations using deep neural networks

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A modified error in constitutive equation approach for frequency-domain viscoelasticity imaging using interior data

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Error Estimate Procedure in the Finite Element Method and Applications

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A variational method for parameter identification

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Validation of structural dynamics models containing uncertainties

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Real-time updating of structural mechanics models using Kalman filtering, modified constitutive relation error, and proper generalized decomposition

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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Large elastic deformations of isotropic materials IV. further developments of the general theory

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Modified constitutive relation error identification strategy for transient dynamics with corrupted data: the elastic case

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A thermodynamics-informed active learning approach to perception and reasoning about fluids

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Identifiers

10.1002/nme.7439

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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:6499924

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