Transfer and physics-informed learning to overcome data scarcity

par Mathilde Mougeot (ensIIE & ENS Paris-Saclay.)

mardi 3 févr. 2026, 11:15 → 12:15 Europe/Paris

Salle K. Johnson (1R3, 1er étage)

In recent years, considerable progress has been made in implementing decision support procedures based on machine learning methods through the use of very large databases and learning algorithms. In many application areas, the available databases are modest in size, raising the question of whether it is reasonable, in this context, to seek to develop powerful tools based on machine learning techniques. This presentation introduces models that leverage various types of knowledge through pre-trained alternative models, targeted observations, or physics in order to implement effective machine learning models in a context of data scarcity. Several industrial applications are used to illustrate benefits of these approaches.