A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites
Artikel i vetenskaplig tidskrift, 2023

The macroscopic response of short fiber reinforced composites (SFRCs) is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path-dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of SFRCs, given the microstructural parameters and the strain path. Micromechanical mean-field simulations are conducted to create a database for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations.

recurrent neural networks

short fiber composites

cyclic deformation

path-dependent plasticity

deep learning



J. Friemann

Student vid Chalmers

B. Dashtbozorg

Technische Universiteit Eindhoven

Martin Fagerström

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

S. M. Mirkhalaf

Göteborgs universitet

International Journal for Numerical Methods in Engineering

0029-5981 (ISSN) 1097-0207 (eISSN)

Vol. 124 10 2292-2314


Teknisk mekanik

Annan fysik

Bioinformatik (beräkningsbiologi)



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