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Solving inverse bimodular problems via artificial neural network

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This article suggests the utilization of artificial neural network to estimate bimodular constitutive parameters, including extensional/compressive moduli, and extensional/compressive Poisson's ratios. By combining a smoothing function with an initial stress scheme, solutions of the direct bimodular problems are provided by finite element (FE) techniques, and are employed as input to train the networks. One- and two-dimensional numerical examples are presented to illustrate the performance of the network, and good results are achieved. Several factors affecting network performance are discussed.
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Keywords: artificial neural network; bimodularity; inverse problem

Document Type: Research Article

Affiliations: State Key Lab of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116023, People's Republic of China

Publication date: 2009-12-01

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