Master Thesis Online
Master thesis and internship[BR]- Master's thesis : Using Machine Learning algorithms to accelerate Phase-Field applied to compute microstructure evolution[BR]- Internship
Master_thesis_Victor_Borlaf_New.pdf
Borlaf Nieto, Víctor 
Promoteur(s) :
Habraken, Anne
Date de soutenance : 4-sep-2023/5-sep-2023 • URL permanente : http://hdl.handle.net/2268.2/17995
Détails
| Titre : | Master thesis and internship[BR]- Master's thesis : Using Machine Learning algorithms to accelerate Phase-Field applied to compute microstructure evolution[BR]- Internship |
| Titre traduit : | [fr] En utilisant algorithmes de Machine Learning pour accélérer Phase-Field appliqué à la computation de l'èvolution de microstructures |
| Auteur : | Borlaf Nieto, Víctor
|
| Date de soutenance : | 4-sep-2023/5-sep-2023 |
| Promoteur(s) : | Habraken, Anne
|
| Membre(s) du jury : | Duchene, Laurent
Mertens, Anne
|
| Langue : | Anglais |
| Nombre de pages : | 83 |
| Mots-clés : | [en] Phase-field [en] Artificial intelligence [en] Neural networks [en] Variational autoencoder [en] AlSi10Mg [en] Laser powder bed fusion [en] LPBF [en] Python [en] Yield stress |
| Discipline(s) : | Ingénierie, informatique & technologie > Science des matériaux & ingénierie |
| Centre(s) de recherche : | Université de Liège |
| Public cible : | Chercheurs Professionnels du domaine Etudiants Grand public Autre |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Diplôme : | Cours supplémentaires destinés aux étudiants d'échange (Erasmus, ...) |
| Faculté : | Mémoires de la Faculté des Sciences appliquées |
Résumé
[en] By analyzing the features of the results obtained by the Phase-Field software of the evolution of a microstructure, create a Machine Learning based algorithm that reduces computation time with small error disparity.
Fichier(s)
Document(s)
Victor Borlaf - Master Thesis.pdf
Description: -
Taille: 13.01 MB
Format: Adobe PDF
Cite this master thesis
APA
Borlaf Nieto, V. (2023). Master thesis and internship[BR]- Master's thesis : Using Machine Learning algorithms to accelerate Phase-Field applied to compute microstructure evolution[BR]- Internship. (Unpublished master's thesis). Université de Liège, Liège, Belgique. Retrieved from https://matheo.uliege.be/handle/2268.2/17995
Chicago
All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.