Reduced Order Model Enhanced Source Iteration with Synthetic Acceleration for Parametric Radiative Transfer Equation

Abstract

Applications such as uncertainty quantification and optical tomography, require solving the radiative transfer equation (RTE) many times for various parameters. Efficient solvers for RTE are highly desired. Source Iteration with Synthetic Acceleration (SISA) is a popular and successful iterative solver for RTE. Synthetic Acceleration (SA) acts as a preconditioning step to accelerate the convergence of Source Iteration (SI). After each source iteration, classical SA strategies introduce a correction to the macroscopic particle density by solving a low order approximation to a kinetic correction equation. For example, Diffusion Synthetic Acceleration (DSA) uses the diffusion limit. However, these strategies may become less effective when the underlying low order approximations are not accurate enough. Furthermore, they do not exploit low rank structures concerning the parameters of parametric problems. To address these issues, we propose enhancing SISA with data-driven ROMs for the parametric problem and the corresponding kinetic correction equation. First, the ROM for the parametric problem can be utilized to obtain an improved initial guess. Second, the ROM for the kinetic correction equation can be utilized to design a novel SA strategy called ROMSAD. In the early stage, ROMSAD adopts a ROM based approximation, which builds on the kinetic description of the correction equation and leverages low rank structures concerning the parameters. This ROM-based approximation has greater efficiency than DSA in the early stage of SI. In the later stage, ROMSAD automatically switches to DSA to leverage its robustness. Additionally, we propose an approach to construct the ROM for the kinetic correction equation without directly solving it. In a series of of numerical tests, we compare the performance of the proposed methods with SI-DSA and DSA preconditioned GMRES solver.