Scientific deep learning for Anomaly Detection in ductile DAmage Modeling applied to metal forming // Scientific deep learning for Anomaly Detection in ductile DAmage Modeling applied to metal forming

.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Damage is a particular form of anomaly in material forming. These anomalies
come from materials microstructure heterogeneity that drives ductile damage mechanisms. We propose to combine deep learning for anomaly detection and mechanical modeling of damage. This work is limited to the use of synthetic data produced with mechanical models calibrated in the context of previous work in materials mechanics. However, these models remain imperfect, in particular for dealing with recycled materials or, in general, materials with a high variability of their physical properties.

In this case, an anomaly may be caused by unusual properties or an unsuitable mechanical model. The anomalies will be identified as cases out-of-distribution of so-called normal data. The objective of this project is to develop: (i) self-supervised learning of a latent space of normal data, (ii) an anomaly detection task using this latent space, (iii) a final stage of scientific explanation of the causes of anomalies based on explainable AI. All this in the context of large deformations of point cloud.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Début de la thèse : 01/10/2025
WEB : https://www.cemef.minesparis.psl.eu/wp-content/uploads/2025/03/ADDAM.pdf

Financement d'un établissement public Français

Mines Paris-PSL

364 SFA - Sciences Fondamentales et Appliquées

.
Master degree in Applied Mathematics or in Computational Mechanics