Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate

Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element analysis (FE2) due to the exceedingly high computation...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering 2023-03, Vol.407, p.115934, Article 115934
Hauptverfasser:	Maia, M.A., Rocha, I.B.C.M., Kerfriden, P., van der Meer, F.P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Neural Networks (ANNs) Computer Science Heterogeneous materials Multiscale Path-dependency
Online-Zugang:	Volltext
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