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DEFI Research team

Shape reconstruction and identification

Team presentation

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.

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.

 

Research themes

  • Non-iterative methods for inverse boundary value problems, inverse scattering problems and imaging (Samplings methods for multistatic data at a fixed frequency, Asymptotic methods, Qualitative estimates for target identification problems, ...)
  • Shape and topological optimization methods with application to inverse problems (Homogenization and small amplitude homogenization methods, Level Set methods, Toplogical gradient methods, ...)
  • Forward and inverse models for Diffusion MRI
  • Regularization and stability issues for ill-posed problems and imaging (Stability estimates, Methods of total variation, Edge preserving image reconstructions, ...)
  • Forward/Backward uncertainty quantification methods for optimization/inversion problems in the context of expensive computer codes.

     

     

International and industrial relations

  • EDF, DGA, Xenocs, Neurospin, SAFRAN, ArianeGroup, Renault, SystemX, Thales
  • Universities of Delaware (USA), Rutgers University (USA), University of Bremen (Germany), University of Goettingen (Germany), ENIT (Tunisia), ITU (Turkey), University of Genova (Italy), von Karman Institute for Fluid Dynamics (Belgium), University of Zurich (Swisserland), EPFL University (Swisserland)