  We present an equation-free dynamic renormalization approach to the
computational study of coarse-grained, self-similar dynamic behavior in
multidimensional particle systems. The approach is aimed at problems for which
evolution equations for coarse-scale observables (e.g. particle density) are
not explicitly available. Our illustrative example involves Brownian particles
in a 2D Couette flow; marginal and conditional Inverse Cumulative Distribution
Functions (ICDFs) constitute the macroscopic observables of the evolving
particle distributions.
