Predicting elemental stiffness matrix of FG nanoplates using Gaussian process regression based surrogate model in framework of layerwise model

DOI10.1016/j.enganabound.2022.08.001OpenAlexW4290964092WikidataQ113875154 ScholiaQ113875154MaRDI QIDQ2085990

Li Li, Aman Garg, Mohamed-Ouejdi Belarbi, Ankit Singh, Abdelouahed Tounsi, Tanmoy Mukhopadhyay

Publication date: 19 October 2022

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.08.001

zbMATH Keywords

stiffness matrixmachine learningsurrogate modelFG nanoplateGPR

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Biology and other natural sciences (92-XX)

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Cites Work

• Natural frequency analysis of functionally graded rectangular nanoplates with different boundary conditions via an analytical method
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• Aerodynamic analysis of temperature-dependent FG-WCNTRC nanoplates under a moving nanoparticle using meshfree finite volume method
• Numerical study of the effects of twisted-tape inserts on heat transfer parameters and pressure drop across a tube carrying graphene oxide nanofluid: an optimization by implementation of artificial neural network and genetic algorithm
• Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders
• On refined theories of composite laminates

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