Summary of the project: 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.
