Article (Scientific journals)
Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]
Fetni, Seifallah; Delahaye, Jocelyn; Habraken, Anne
2023In Software Impacts, 17, p. 100563
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Keywords :
Deep learning; Image generation and processing; LSTM; PCA; Python development; Software
Abstract :
[en] The passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn–Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions.
Research center :
UEE - Urban and Environmental Engineering - ULiège [BE]
Disciplines :
Computer science
Author, co-author :
Fetni, Seifallah ;  UEE Research Unit, University of Liège, Belgium
Delahaye, Jocelyn ;  Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Habraken, Anne  ;  Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
Language :
English
Title :
Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]
Publication date :
September 2023
Journal title :
Software Impacts
eISSN :
2665-9638
Publisher :
Elsevier B.V.
Volume :
17
Pages :
100563
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
Funders :
ULiège - Université de Liège [BE]
Funding text :
Uliege FSA Faculty Research grant and CECI. Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS), Belgium under Grant No. 2.5020.11 and by the Walloon Region, Belgium . A special thank to Mr. David Colignon for his availability and great support to successfully achieve computational tasks.
Available on ORBi :
since 18 October 2023

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