Finite electro-elasticity with physics-augmented neural networks
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Publication:2083132
DOI10.1016/j.cma.2022.115501OpenAlexW4293155360MaRDI QIDQ2083132
Dominik K. Klein, Jesús Martínez-Frutos, Oliver Weeger, Rogelio Ortigosa
Publication date: 10 October 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.05139
homogenizationconstitutive modelingelectro-active polymersnonlinear electro-elasticityphysics-augmented machine learning
Artificial neural networks and deep learning (68T07) Composite and mixture properties (74E30) Electromagnetic effects in solid mechanics (74F15)
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Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics ⋮ Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement ⋮ \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining ⋮ Gradient enhanced Gaussian process regression for constitutive modelling in finite strain hyperelasticity ⋮ Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria ⋮ Automated discovery of generalized standard material models with EUCLID ⋮ Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity
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