Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints

DOI10.1002/nme.6992arXiv2112.02077OpenAlexW4224278884MaRDI QIDQ6070057

Unnamed Author, Tommy Sewell, WaiChing Sun, Ran Ma, Nikolaos N. Vlassis

Publication date: 20 November 2023

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.02077

zbMATH Keywords

neural networkSobolev normstress measurephysical constraintelastic stored free energy functionaltangential constraint

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Nonlinear elasticity (74B20) Crystalline structure (74E15) Molecular, statistical, and kinetic theories in solid mechanics (74A25)

Related Items (3)

Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

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