Gaia Cevini

Performance evaluation of 3D FEM-based simulators in modelling scattering problems with metamaterials

This contribution is focused on the evaluation of the performances of three-dimensional finite element methods in solving electromagnetic scattering problems involving metamaterials. Finite element methods can be effectively used to provide a convergent approximation for investigating the unusual properties of metamaterials. However, an exhaustive assessment of the performances of the method, both in terms of its accuracy and of the required computational resources, has not been performed yet when metamaterials are involved. This paper addresses this topic by considering the 3D electromagnetic finite element modelling of the scattering properties of simple objects made up of metamaterials.

TDFEM analysis of the scattering properties of shielding structures

This paper is focused on the application of the time-domain finite element method (TDFEM) to study over a wide frequency band the scattering from different shielding structures. The present investigation is meant to provide some considerations on how the scattering and shielding characteristics depend on the geometric as well as on the dielectric properties of the shields. The finite element method is used to model the structures considered and to provide the transient electromagnetic fields. The finite element formulation integrates a time-domain absorbing boundary condition to truncate the unbounded numerical domain. Numerical results, concerning some 2D modellizations of different shielding structures, will be provided

A Neural Network Approach for Electromagnetic Diagnostic Applications

In this paper, a neural network approach is applied to a typical electromagnetic diagnostic problem consisting in the prediction of the electromagnetic field absorption inside a dielectric phantom. The approach has been tested by considering, at the input of the neural network, the values of the incident electric field at a fixed number of locations. Also phaseless measures have been taken into account. The outputs are some features describing the electromagnetic absorption, such as the peak amplitude of the induced field, the location of the absorption peak and a measure of the extension of the induced spot inside the phantom. Preliminary results show that the approach shows a satisfactory accuracy in reconstructing the selected absorption features, and that it is able to estimate these characteristics very quickly.

Assessment of the robustness of a neural network approach for the electromagnetic reconstruction of buried cylinders

The present paper is intended to give some insights into the application of a neural network approach to reconstruct buried cylinders by exploiting time-domain electromagnetic data acquired when the source is moving above the half-space. The robustness of the approach has been tested against variations in the a-priori information used to build the reconstruction algorithm in order to detect its limits of applicability.

Electromagnetic localization of dielectric targets in a 3D geometry by means of a neural network approach

This paper deals with an electromagnetic inverse scattering problem, i.e. the localization of a dielectric target in a 3D half-space. The approach chosen to face the problem makes use of a learning-by-examples procedure based on a neural network algorithm. The neural network is trained to reconstruct the coordinates of the barycenter of the object starting from the acquisition of the back-scattered electromagnetic field at a number of observation positions. The performances of the approach are analyzed by considering different types of input data and different neural networks setups