Effective Parameter Dimension via Bayesian Model Selection in the Inverse Acoustic Scattering Problem

Abstract EN

We address a prototype inverse scattering problem in the interface of applied mathematics, statistics, and scientific computing.

We pose the acoustic inverse scattering problem in a Bayesian inference perspective and simulate from the posterior distribution using MCMC.

The PDE forward map is implemented using high performance computing methods.

We implement a standard Bayesian model selection method to estimate an effective number of Fourier coefficients that may be retrieved from noisy data within a standard formulation.