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Erschienen in:

2024 | OriginalPaper | Buchkapitel

Data-Driven Multi-scale Numerical Homogenization

verfasst von : Georgios E. Stavroulakis, Eleftheria Bletsogianni

Erschienen in: Proceedings of the Joint International Conference: 10th Textile Conference and 4th Conference on Engineering and Entrepreneurship

Verlag: Springer Nature Switzerland

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Abstract

Homogenization for complex, nonlinear materials, including composites and textiles, is considered. The effect of the microstructure, including nonlinearity, for various loadings is transferred to the homogenized medium through the Representative Volume Element technique. The classical approach through nested finite element analysis, the so-called FEM2 method, is very expensive. Data-driven techniques have been proposed, where the homogenization is replaced by a surrogate model based on data generated through selected numerical or physical experiments. An artificial neural network surrogate constitutive model leads to a FEANN method. Furthermore data-driven solution methods have been recently proposed, as a development of the LATIN iterative method. A short review of recent contributions is presented, with examples from composites, including woven composites and auxetics. Research topics will be discussed, including the development of Physics Informed Neural Network surrogates.

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Vorheriges Kapitel The Influence of Bio-Innovative Pretreatment to Cotton Fabric Whiteness

Nächstes Kapitel Auxetics, Theory and Textile Applications

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Titel: Data-Driven Multi-scale Numerical Homogenization
verfasst von: Georgios E. Stavroulakis
Eleftheria Bletsogianni
Verlag: Springer Nature Switzerland
Buch: Proceedings of the Joint International Conference: 10th Textile Conference and 4th Conference on Engineering and Entrepreneurship
Print ISBN: 978-3-031-48932-7

Electronic ISBN: 978-3-031-48933-4

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-48933-4_49

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