Advanced Interacting Sequential Monte Carlo Sampling for Inverse Scattering
type: Dataset
contact point: Organization
  fn: CCSD
keyword: inverse-scattering
keyword: preprints-working-papers-
keyword: acm-g-mathematics-of-computingg3-probability-and-statisticsg313-statistical-computing
keyword: bayesian-inference
keyword: acm-g-mathematics-of-computingg3-probability-and-statisticsg37-probabilistic-algorithms-i
keyword: acm-g-mathematics-of-computing
keyword: infoeu-reposemanticspreprint
keyword: statapstatistics-statapplications-statap
keyword: metamodeling
keyword: sequential-monte-carlo
landing page: https://inria.hal.science/hal-00779847
description: 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.
issued: 2026-05-14T23:57:38.682442
language: en
modified: 2026-05-14T23:57:38.682447

test_moissonnage_selune
type: Agent

Advanced Interacting Sequential Monte Carlo Sampling for Inverse Scattering
type: Distribution
access URL: https://inria.hal.science/hal-00779847
format: HTML
issued: 2026-05-14T23:57:38.717376
modified: 2026-05-14T23:57:38.660165