  The following electromagnetism (EM) inverse problem is addressed. It consists
in estimating local radioelectric properties of materials recovering an object
from global EM scattering measurements, at various incidences and wave
frequencies. This large scale ill-posed inverse problem is explored by an
intensive exploitation of an efficient 2D Maxwell solver, distributed on high
performance computing machines. Applied to a large training data set, a
statistical analysis reduces the problem to a simpler probabilistic metamodel,
on which Bayesian inference can be performed. Considering the radioelectric
properties as a hidden dynamic stochastic process, that evolves in function of
the frequency, it is shown how advanced Markov Chain Monte Carlo methods,
called Sequential Monte Carlo (SMC) or interacting particles, can take benefit
of the structure and provide local EM property estimates.
