Reference : A Recurrent Neural Network-based Surrogate Model for History-Dependent Multi-scale Si...
Scientific conferences in universities or research centers : Scientific conference in universities or research centers
Engineering, computing & technology : Materials science & engineering
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/2268/266181
A Recurrent Neural Network-based Surrogate Model for History-Dependent Multi-scale Simulations of Composite Materials
English
Wu, Ling mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3) >]
Noels, Ludovic mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3) >]
8-Dec-2021
International
BRAIA Lecture Series on Technology Frontier
8 December 2021
BRAIA
Northwestern Polytechnical University
China
[en] Multiscale ; Artificial Intelligence ; Recurrent Neural Network
[en] In order to make Computational homogenization affordable, pre-off-line finite element simulations are conducted on the mesoscale problem in order to build a synthetic database that can, in turn, be used to train surrogate models, which can be used as a constitutive law on a classical finite element simulation, speeding up the multi-scale process by several orders.

Artificial neural networks (NNWs) offer the possibility to serve as a surrogate model, but a difficulty arises for elasto-plasticity because of its history-dependency. This difficulty can be solved by considering a Recurrent Neural Network (RNN), which uses sequential information [1]. Nevertheless, in order to be accurate under multi-dimensional non-proportional loading conditions, a sufficiently wide database is required in order to perform the training. To this end, a sequential training synthetic database is obtained from finite element simulations on an elasto-plastic RVE subjected to random loading paths. The RNN predictions are thus found to be in agreement with the FE2 simulations, while reducing the computational cost by 4 orders.

Nevertheless, such a paradigm is essentially used as a mapping between the macro-stress and macro-strain tensors of the micro-scale boundary value response and the micro-structure information could not be recovered in a so-called localization step. We thus also develop Recurrent Neural Networks (RNNs)-based surrogate of the local micro-structure state variables for complex loading scenarios [2]. In order to address the curse of dimensionality arising because of the large amount of internal state variables in the micro-structure, we enrich the RNN with PCA dimensionality reduction and dimensionality break down, i.e. the use of several RNNs instead of a single one. The sequential training strategy is optimized to allow for GPU usage.
Aérospatiale et Mécanique - A&M
This project has received funding from the European Union´s Horizon 2020 research and innovation programme under grant agreement No. 862015 for the project “Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM)” of the H2020-EU.1.2.1. - FET Open Programme.
Professionals ; Students
http://hdl.handle.net/2268/266181
H2020 ; 862015 - MOAMMM - Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials

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