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
Borlaf Nieto, Víctor 
Promotor(s) :
Habraken, Anne
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/17995
Master_thesis_Victor_Borlaf_New.pdfDetails
| Title : | Master thesis and internship[BR]- Master's thesis : Using Machine Learning algorithms to accelerate Phase-Field applied to compute microstructure evolution[BR]- Internship |
| Translated title : | [fr] En utilisant algorithmes de Machine Learning pour accélérer Phase-Field appliqué à la computation de l'èvolution de microstructures |
| Author : | Borlaf Nieto, Víctor
|
| Date of defense : | 4-Sep-2023/5-Sep-2023 |
| Advisor(s) : | Habraken, Anne
|
| Committee's member(s) : | Duchene, Laurent
Mertens, Anne
|
| Language : | English |
| Number of pages : | 83 |
| Keywords : | [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) : | Engineering, computing & technology > Materials science & engineering |
| Research unit : | Université de Liège |
| Target public : | Researchers Professionals of domain Student General public Other |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Cours supplémentaires destinés aux étudiants d'échange (Erasmus, ...) |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[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.
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