  We explore a computational approach to coarse graining the evolution of the
large-scale features of a randomly forced Burgers equation in one spatial
dimension. The long term evolution of the solution energy spectrum appears
self-similar in time. We demonstrate coarse projective integration and coarse
dynamic renormalization as tools that accelerate the extraction of macroscopic
information (integration in time, self-similar shapes, and nontrivial dynamic
exponents) from short bursts of appropriately initialized direct simulation.
These procedures solve numerically an effective evolution equation for the
energy spectrum without ever deriving this equation in closed form.
