A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures

DOI10.1016/j.apm.2022.02.036zbMath1503.74083OpenAlexW4220689457MaRDI QIDQ2109538

Qui X. Lieu, Hau T. Mai, Joowon Kang, Jaehong Lee

Publication date: 21 December 2022

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2022.02.036

zbMATH Keywords

optimizationneural networksloss functionpotential energytrussgeometrical and material nonlinearities

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Thin bodies, structures (74K99)

Related Items (1)

Optimum design of nonlinear structures via deep neural network-based parameterization framework

Uses Software

• Adam
• TOuNN

Cites Work

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