Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

El Fetni, Seifallah; Habraken, Anne; Duchene, Laurent

doi:10.1016/j.commatsci.2022.111820

Article (Scientific journals)

Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

El Fetni, Seifallah; Habraken, Anne; Duchene, Laurent

2022 • In Computational Materials Science, 216 (January 2023)

Peer Reviewed verified by ORBi Dataset

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https://hdl.handle.net/2268/295442

DOI
10.1016/j.commatsci.2022.111820

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Keywords :

Phase field; Spinodal decomposition; LSTM; GRU; Auto-encoders; PCA; HPC

Abstract :

[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

Development Goals :

9. Industry, innovation and infrastructure

Additional URL :

https://www.sciencedirect.com/science/article/pii/S0927025622005316#GS1

Funders :

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.

Data Set :

https://gitlab.uliege.be/S.Fetni/datasets-for-the-auto-encoder_lstm-project

Available on ORBi :

since 06 October 2022

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