A neurocomputing model for the elastoplasticity
type: Item
instance of: scholarly article
title: A neurocomputing model for the elastoplasticity
zbMATH Open document ID: 1021.74044
DOI: 10.1016/S0045-7825(99)00091-2
author: Daoheng Sun
author: Qiao Hu
author: Hao Xu
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2000-07-10T00:00:00Z
review text: Using the parametric variational principle for elastoplasticity, the authors propose a new variational principle called here ``two-type variable variational principle'', so that the neural networks theory can be used to solve the corresponding elastoplastic problem. For the two-type-variable minimum potential energy principle, the energy function and the constraint conditions of the neural networks are built up, and the structure and parameters are given. As an example, the authors consider the neurocomputation of a simple truss. It is shown that the neurocomputed conclusions are in good agreement with theoretical solutions. The elastoplastic solutions can be obtained within an elapsed nano-second-order time of the circuit time-constant.
Mathematics Subject Classification ID: 74S05
Mathematics Subject Classification ID: 74C05
Mathematics Subject Classification ID: 74G65
zbMATH DE Number: 1418236
zbMATH Keywords: elastoplastic truss
zbMATH Keywords: two-type variable variational principle
zbMATH Keywords: neural networks
zbMATH Keywords: minimum potential energy principle
zbMATH Keywords: energy function
reviewed by: Ladislav Prášek
MaRDI profile type: Publication
cites work: Neural networks and physical systems with emergent collective computational abilities.
cites work: Neural networks for computing in fracture mechanics. Methods and prospects of applications
full work available at URL: s0045-7825(99)00091-2
OpenAlex ID: W2054437877
Wikidata QID: Q127310370
description: scientific article; zbMATH DE number 1418236