A relevant class of radiative transfer problems for polarized radiation is
linear, or can be linearized, and can
thus be reframed as linear systems once discretized.
In this context, depending on the considered physical models,
there are both highly coupled and computationally expensive
problems, for which state-of-the-art
iterative methods struggle to converge, and lightweight ones, for which
solutions can be obtained efficiently. This work aims to exploit lightweight physical models
as preconditioners for iterative solution strategies to obtain accurate
and fast solutions for more complex problems. We considered a highly coupled linear transfer problem for
polarized radiation, which we solved iteratively using a matrix-free generalized
minimal residual (GMRES) method.
Different preconditioners and initial guesses, designed in a physics-based
framework, are proposed and analyzed.
The action of preconditioners was also
computed by applying GMRES. The overall approach thus
consists of two nested GMRES iterations, one for the original problem and one for its lightweight version.
As a benchmark, we considered the modeling
of the intensity and polarization of the solar
Ca i AA line, the Sr ii AA line, and the Mg ii h k lines
in a semi-empirical 1D atmospheric model, accounting for partial
frequency redistribution effects in scattering processes and
considering a general
angle-dependent treatment. Numerical experiments show
that using tailored preconditioners based on simplified models of the considered problem has a noticeable
impact, reducing the number of iterations to convergence by a factor of 10-20. By designing efficient preconditioners in a physics-based context,
it is possible to significantly improve the convergence of
iterative processes,
obtaining fast and accurate numerical solutions to the considered problems.
The presented approach is general, requiring only the selection of an appropriate lightweight model, and can be applied to a larger class of radiative transfer problems in combination with arbitrary iterative procedures.