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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Machine Learning Control Design for Elastic Composite Materials

verfasst von : Sebastián Ossandón, Mauricio Barrientos, Camilo Reyes

Erschienen in: Computational Science – ICCS 2021

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Automated Method for Evaluating Neural Network’s Attention Focus
Nächstes Kapitel Optimize Memory Usage in Vector Particle-In-Cell (VPIC) to Break the 10 Trillion Particle Barrier in Plasma Simulations
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Metadaten
Titel
Machine Learning Control Design for Elastic Composite Materials
verfasst von
Sebastián Ossandón
Mauricio Barrientos
Camilo Reyes
Copyright-Jahr
2021
Buch
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77963-4

Electronic ISBN: 978-3-030-77964-1

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-77964-1_34