TY - GEN
T1 - Machine Learning Control Design for Elastic Composite Materials
AU - OSSANDON VELIZ, SEBASTIAN EDUARDO
AU - BARRIENTOS BARRIA, MAURICIO ANDRES
AU - Reyes, Camilo
N1 - Funding Information:
S. Ossand?n acknowledges support from the European Union?s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777778: MATHROCKS PROJECT.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Composite materials
KW - Eigenfrequencies of the elasticity operator
KW - Finite element method
KW - Inverse problems
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85111385292&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77964-1_34
DO - 10.1007/978-3-030-77964-1_34
M3 - Conference contribution
AN - SCOPUS:85111385292
SN - 9783030779634
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 437
EP - 451
BT - Computational Science – ICCS 2021 - 21st International Conference, Proceedings
A2 - Paszynski, Maciej
A2 - Kranzlmüller, Dieter
A2 - Kranzlmüller, Dieter
A2 - Krzhizhanovskaya, Valeria V.
A2 - Dongarra, Jack J.
A2 - Sloot, Peter M.A.
A2 - Sloot, Peter M.A.
A2 - Sloot, Peter M.A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Computational Science, ICCS 2021
Y2 - 16 June 2021 through 18 June 2021
ER -