Recurrent neural network plasticity models: unveiling their common core through multi-task learning

DOI10.1016/j.cma.2024.116991zbMATH Open1539.7408MaRDI QIDQ6550155

Dirk Mohr, Unnamed Author

Publication date: 4 June 2024

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

zbMATH Keywords

recurrent neural networkartificial intelligenceplasticitymulti-task learningtransfer learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Plastic materials, materials of stress-rate and internal-variable type (74C99)

Cites Work

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• Computational inelasticity
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
• Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials
• Transfer learning of recurrent neural network‐based plasticity models

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