Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity

DOI10.1007/s00466-023-02429-1zbMath1545.74088MaRDI QIDQ6584871

Ramin Bostanabad, Shirin Hosseinmardi, Libo Wang, Diran Apelian, Shiguang Deng

Publication date: 8 August 2024

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

zbMATH Keywords

path dependencemicroscale simulationdeep learning modelirreversible elasto-plastic hardening/softeningtraining data generation

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Fluid-solid interactions (including aero- and hydro-elasticity, porosity, etc.) (74F10) Anelastic fracture and damage (74R20) Numerical and other methods in solid mechanics (74S99)

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