Physics informed neural networks for continuum micromechanics

DOI10.1016/j.cma.2022.114790OpenAlexW4226480439MaRDI QIDQ2138812

H. Wessels, Alexander Henkes, Rolf Mahnken

Publication date: 12 May 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.07374

zbMATH Keywords

domain decompositionmicromechanicsadaptivityheterogeneous materialsphysics informed neural networks\(\mu\)CT-scans

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Finite element methods applied to problems in solid mechanics (74S05) Micromechanics of solids (74M25) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)

Related Items (20)

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Uses Software

• Python
• TensorFlow
• PINN
• XPINNs
• PRMLT
• Stiff-PINN
• DeepONet
• PINNsNTK

Cites Work

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• Deep learning in fluid dynamics

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