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Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks

Weber, Patrick 1; Wagner, Werner 1; Freitag, Steffen 1
1 Institut für Baustatik (IBS), Karlsruher Institut für Technologie (KIT)

Abstract:

In recent years, a lot of progress has been made in the field of material modeling with artificial neural networks (ANNs). However, the following drawbacks persist to this day: ANNs need a large amount of data for the training process. This is not realistic, if real world experiments are intended to be used as data basis. Additionally, the application of ANN material models in finite element (FE) calculations is challenging because local material instabilities can lead to divergence within the solution algorithm. In this paper, we extend the approach of constrained neural network training from [28] to elasto-plastic material behavior, modeled by an incrementally defined feedforward neural network. Purely stress and strain dependent equality and inequality constraints are introduced, including material stability, stationarity, normalization, symmetry and the prevention of energy production. In the Appendices, we provide a comprehensive framework on how to implement these constraints in a gradient based optimization algorithm. We show, that ANN material models with training enhanced by physical constraints leads to a broader capture of the material behavior that underlies the given training data. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000157997
Veröffentlicht am 05.05.2023
Originalveröffentlichung
DOI: 10.1007/s00466-023-02316-9
Scopus
Zitationen: 14
Web of Science
Zitationen: 13
Dimensions
Zitationen: 17
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Baustatik (IBS)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 0178-7675, 1432-0924
KITopen-ID: 1000157997
Erschienen in Computational Mechanics
Verlag Springer
Vorab online veröffentlicht am 07.04.2023
Nachgewiesen in Dimensions
Scopus
Web of Science
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