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

DOI10.1007/s00466-022-02260-0zbMath1517.74090arXiv2207.01045MaRDI QIDQ6101611

Jörg Brummund, Lennart Linden, Karl A. Kalina, Markus Kästner

Publication date: 1 June 2023

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.01045

zbMATH Keywords

fiber-reinforced compositerepresentative volume elementHelmholtz free energycomputational homogenizationanisotropic finite strain hyperelasticitydecoupled multiscale scheme

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Nonlinear elasticity (74B20) Finite element methods applied to problems in solid mechanics (74S05) Numerical and other methods in solid mechanics (74S99)

Related Items (5)

Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation ⋮ Efficient multiscale modeling of heterogeneous materials using deep neural networks ⋮ Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity

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