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A mapping of the process on a continuous configuration space to the symbolicrepresentation of the motion on a discrete state space will be combined with aniterative aggregation and disaggregation (IAD) procedure to obtain steady statedistributions of the process. The IAD speeds up the convergence to the uniteigenvector, which is the steady state distribution, by forming smalleraggregated matrices whose unit eigenvector solutions are used to refineapproximations of the steady state vector until convergence is reached. Thismethod works very efficiently and can be used together with distributed orparallel computing methods to obtain high resolution images of the steady statedistribution of complex atomistic or energy landscape type problems. The methodis illustrated in two numerical examples. In the first example the transitionmatrix is assumed to be known. The second example represents an overdampedBrownian motion process subject to a dichotomously changing external potential.

An iterative aggregation and disaggregation approach to the calculation of steady-state distributions of continuous processes