Objectives

For the last few years, machine learning techniques have fastly emerged as a disruptive way to analyze complex systems in absence of models, on the basis of observational data. Despite their potential, the naive application to physical problems has however revealed some critical drawbacks, such as the lack of accuracy, robustness and explainability, the expensive training or the inability to intrinsically verify some established physical principles.

To overcome these limitations, the hybridization of machine learning techniques with classical scientific computing methodologies is a promising research axis.

In this context, the objective of the thematic semester Machine Learning + Simulation is to contribute to the development of these activities, by building bridges between specialists of machine learning and experts in simulation for various application fields.

Thematic semester

Events

The thematic semester Machine Learning + Simulation at Université Côte d’Azur is mainly composed of some scientific events, related to the interaction, coupling and hybridization of machine learning techniques and simulation methodologies.

The semester will be hosting in 2026 the following three events:

• Spring workshop on “Machine learning for simulation”
• Summer school “Machine learning + simulation”
• Fall workshop on “Hybridization of machine learning and simulation”

Internships

A set of Master2 internships, whose topics are in line with the semester, will be granted in 2026 at Université Côte d’Azur.

Visits and seminars

Some professors / researchers will be invited in 2026, giving seminars on selected topics.

Sponsors

This project is supported by the Excellence Academy “Complex Systems” at Université Côte d’Azur and the following sponsors:

Organisation team

The thematic semester is organized by:

• Didier Auroux (LJAD, MSI)
• Silvia Bottini (INRAE, Smile team)
• Guillaume Cordonnier (Inria, GraphDeco team)
• Régis Duvigneau (Inria, LJAD, Acumes team)[chair]
• Elie Hachem (CEMEF, CFL team)
• Abdelrahman Ijjeh (LEAT, CMA team)
• Angelos Mantzaflaris (Inria, Aromath team)
• Julie Regnier (CEREMA)
• Thomas Rey (LJAD)
• Rémy Sun (Inria, I3S, Maasai team)
• Jonathan Viquerat (CEMEF, CFL team)