Doctorant sous la direction de D. LESSELIER

Titre de la thèse: Diagnostic within a micro-structured 3-dimensional antenna systems: joint-sparsity inversion and convolutional neural networks
Résumé de la thèse: A finitely set of infinitely long, regularly-distributed dielectric rods is illuminated from the outside by time-harmonic ideal electric line sources at a single microwave frequency and the fields scattered in each experiment are collected accordingly. Some of the rods are missing in unknown fashion, that is, they are associated to a lacunary (damaged) micro-structure, here within the demanding hypothesis of radii of rods and inter-rod distances that are small versus the wavelength of operation. To detect those missing rods in such a complex situation in terms of electromagnetic behavior and achievable resolution, powerful convolutional neural networks (CNN) will be shown to provide for an efficient diagnostic in addition also to the appraisal of the permittivity maps of the micro-structure as a whole. The approach developed herein arises from machine learning within the domain of waves and fields . Provided that a well-designed network architecture, and evidently well training data sets and adequate training method, among other necessary features, good results follow if and in effect only if quite much numerical experimentation on known micro-structures so as to design and validate a network efficient enough, computationally speaking and in terms of the physical output.