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

•A robust and simple unsupervised neural network framework for large deformation analysis of truss structures.•Training data is small in size, independent of sampling procedures, without finite element analysis.•Reliability and stability of the proposed approach for nonlinear analysis without increm...

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Veröffentlicht in:	Applied Mathematical Modelling 2022-07, Vol.107, p.332-352
Hauptverfasser:	Mai, Hau T., Lieu, Qui X., Kang, Joowon, Lee, Jaehong
Format:	Artikel
Sprache:	eng
Schlagworte:	Back propagation networks Boundary conditions Finite element method Geometrical and material nonlinearities Iterative algorithms Iterative methods Loss function Neural networks Nonlinear analysis Optimization Potential energy Robustness Structural response Truss Trusses
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