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Détermination de Formes et Identification

Equipe-projet commune avec Inria Saclay-Ile de France.

La page Inria de l'équipe.

Responsable : Houssem Haddar, Directeur de Recherche Inria.

Research Scientists:

-Marcella Bonazzoli, Chargée de Recherche Inria
-Lucas Chesnel, Chargé de Recherche Inria
-Pietro Marco Congedo, Chargé de Recherche Inria
-Olivier Le Maitre, Directeur de Recherche CNRS
-Jing-Rebecca Li, Directrice de Recherche Inria

Faculty Members:

-Grégoire Allaire, Professeur Ecole polytechnique

External Collaborators:

- Lorenzo Audibert, EDF R&D
- Matthieu Aussal, Ingénieur de recherche Ecole polytechnique

Post-doctorants :

- Giulio Gori
- Jérémy Heleine
- Xiaoli Liu
- Cristóbal Villalobos Guillén

Doctorants :

- Anabel Del Val
- Chengran Fang
- Hugo Girardon
- Nouha Jenhani
- Amal Labidi
- Nicolas Leoni
- Dorian Lerévérend
- Marwa Mansouri
- Paul Novello
- Malo Pocheau
- João Reis
- Mahran Rihani
- Tuan Anh Vu
- Zheyi Yang

Software engineer :

- Syver Døving Agdestein

Assistante administrative :

- Marie Enée


Activités de recherche :

The research activity of our team is dedicated to the design, analysis and implementation of efficient numerical methods to solve inverse and shape/topological optimization problems, eventually including system uncertainties, in connection with acoustics, electromagnetism, elastodynamics, diffusion, and fluid mechanics.

Sought practical applications include radar and sonar applications, bio-medical imaging techniques, non-destructive testing, structural design, composite materials, diffusion magnetic resonance imaging, fluid-driven applications in aerospace/energy fields.

Roughly speaking, the model problem consists in determining information on, or optimizing the geometry (topology) and the physical properties of unknown targets from given constraints or measurements, for instance, measurements of diffracted waves or induced magnetic fields. Moreover, system uncertainties can ben systematically taken into account to provide a measure of confidence of the numerical prediction.

In general this kind of problems is non-linear. The inverse ones are also severely ill-posed and therefore require special attention from regularization point of view, and non-trivial adaptations of classical optimization methods.

We are particularly interested in the development of fast methods that are suited for real-time applications and/or large scale problems. These goals require to work on both the physical and the mathematical models involved and indeed a solid expertise in related numerical algorithms.

Our scientific research interests are the following:

- Theoretical understanding and analysis of the forward and inverse mathematical models, including in particular the development of simplified models for adequate asymptotic configurations.
- The design of efficient numerical optimization/inversion methods which are quick and robust with respect to noise. Special attention will be paid to algorithms capable of treating large scale problems (e.g. 3-D problems) and/or suited for real-time imaging.
- Propose new methods and develop advanced tools to perform uncertainty quantification for optimization/inversion.
- Development of prototype softwares for specific applications or tutorial toolboxes.

We are particularly interested in the development of the following themes:

- Qualitative and quantitative methods for inverse scattering problems.
- Topological optimization methods.
- Forward and inverse models for Diffusion MRI.
- Forward/Backward uncertainty quantification methods for optimization/inversion problems in the context of expensive computer codes.