An iterative aggregation and disaggregation method for the steady state solution of large scale continuous systems

Iterative aggregation and disaggregation (IAD) methods are a class of iterative methods which allow the fast calculation of the eigenvector corresponding to the unit eigenvalue of large stochastic matrices. This makes them particularly appealing to use in high resolution studies of large continuous systems. In this paper it is shown how an IAD matrix method with pre- and post-smoothing steps can be used to obtain the steady state probability distribution of large scale continuous systems. The method is illustrated in two numerical examples of overdamped Brownian motion processes subject to external potentials.