Machine Learning Control Design for Elastic Composite Materials

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Resumen

A novel numerical method, based on a machine learning approach, is used to solve an inverse problem involving the Dirichlet eigenfrequencies for the elasticity operator in a bounded domain filled with a composite material. The inhomogeneity of the material under study is characterized by a vector which is designed to control the constituent mixture of homogeneous elastic materials that compose it. Using the finite element method, we create a training set for a forward artificial neural network, solving the forward problem. A forward nonlinear map of the Dirichlet eigenfrequencies as a function of the vector design parameter is then obtained. This forward relationship is inverted and used to obtain a training set for an inverse radial basis neural network, solving the aforementioned inverse problem. A numerical example showing the applicability of this methodology is presented.

Idioma originalInglés
Título de la publicación alojadaComputational Science – ICCS 2021 - 21st International Conference, Proceedings
EditoresMaciej Paszynski, Dieter Kranzlmüller, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas437-451
Número de páginas15
ISBN (versión impresa)9783030779634
DOI
EstadoPublicada - 2021
Evento21st International Conference on Computational Science, ICCS 2021 - Virtual, Online
Duración: 16 jun. 202118 jun. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12743 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia21st International Conference on Computational Science, ICCS 2021
CiudadVirtual, Online
Período16/06/2118/06/21

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