Plasticity including the Bauschinger effect, studied by a neural network approach

DOI10.1007/BF01212634zbMath0857.73029OpenAlexW2084897902MaRDI QIDQ1814897

Panagiotis D. Panagiotopoulos, Pericles S. Theocaris

Publication date: 24 February 1997

Published in: Acta Mechanica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01212634

zbMATH Keywords

learning algorithmsanisotropic elasticityparameter identificationyield surfaceselliptic paraboloid failure surfaceanisotropic hardening plasticity

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Plastic materials, materials of stress-rate and internal-variable type (74C99)

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A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling ⋮ Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder-decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates ⋮ Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis ⋮ Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics ⋮ Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response ⋮ A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity ⋮ Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response

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