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Material Modeling via Thermodynamics-Based Artificial Neural Networks - MaRDI portal

Material Modeling via Thermodynamics-Based Artificial Neural Networks (Q5021908)

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scientific article; zbMATH DE number 7457735

Language	Label	Description	Also known as
English	Material Modeling via Thermodynamics-Based Artificial Neural Networks	scientific article; zbMATH DE number 7457735

Statements

scholarly article

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Material Modeling via Thermodynamics-Based Artificial Neural Networks (English)

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Victor Maffi-Berthier

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Ioannis Stefanou

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Springer Proceedings in Mathematics & Statistics

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publication date

14 January 2022

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1007/978-3-030-77957-3_16

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Learning non-Markovian physics from data

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Structure-preserving neural networks

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On the micromechanics of deep material networks

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Approximation by superpositions of a sigmoidal function

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Model-free data-driven inelasticity

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Model-free data-driven computational mechanics enhanced by tensor voting

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A multilevel finite element method (FE\(^{2}\)) to describe the response of highly nonlinear structures using generalized continua.

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

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

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

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A thermomechanical framework for constitutive models for rate-independent dissipative materials

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Learning constitutive relations from indirect observations using deep neural networks

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Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

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A manifold learning approach to data-driven computational elasticity and inelasticity

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Data-driven computational mechanics

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Artificial neural network as an incremental nonlinear constitutive model for a finite element code.

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Artificial neural networks in numerical modelling of composites

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

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

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A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

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Thermodynamics with internal variables. I: General concepts. II: Applications

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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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Identifiers

10.1007/978-3-030-77957-3_16

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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:5021908

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