Multi-Particle Collision Algorithm for Solving an Inverse Radiative Problem

Abstract

An inverse radiative transfer problem formulated as a finite dimensional optimization problem, using Multi-Particle Collision Algorithm with a pre-regularization strategy. The radiation propagation in a finite space domain, under isotropic-scattering, assuming plane-parallel geometry is considered. The optical properties, absorption and scattering coefficients, have space dependency. The problem is described by linear Boltzmann equation, considering polar angle discretization and azimuthal symmetry. The forward problem is solved using the discrete ordinates. The inverse problem, reconstruction of the albedo profile, is performed minimizing the square difference between measured radiance and the photon concentration computed from the mathematical forward model emerging from the body. A large number of particles is generated, and those smoother particles are selected. This scheme is called intrinsic regularization. Multi-Particle Collision Algorithm is a stochastic method for global optimization, also called meta-heuristic, and is used to solve the inverse problem. Noiseless and noisy data of the emergent radiation intensities were employed to compute the albedo profile. Good inverse solutions are obtained with the proposed approach.