Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations
[en] In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction (auto-encoders, principal component analysis, Artificial Neural Networks) and time-series analysis (LSTM, Gated Recurrent Unit (GRU)).
Disciplines :
Materials science & engineering
Author, co-author :
El Fetni, Seifallah ; Université de Liège - ULiège > Urban and Environmental Engineering ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
Habraken, Anne ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F ; Université de Liège - ULiège > Département de mécanique des matériaux et structures ; Université de Liège - ULiège > Département de mécanique des matériaux et structures > Service M & S
Duchene, Laurent ; Université de Liège - ULiège > Département ArGEnCo > Analyse multi-échelles dans le domaine des matériaux et structures du génie civil ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
Language :
English
Title :
Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations
Publication date :
2022
Journal title :
Computational Materials Science
ISSN :
0927-0256
eISSN :
1879-0801
Publisher :
Elsevier, Netherlands
Volume :
216
Issue :
January 2023
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
ULiège research council of Sciences and Techniques
Funding text :
As Research Director of FRS-FNRS, AM Habraken acknowledges the support of this institution. The ULiège research council of Sciences and Techniques is acknowledged for the post-doc IN IPD-STEMA 2019 grant of Seifallah Fetni.
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