  We present a general numerical scheme for the practical implementation of
statistical moment closures suitable for modeling complex, large-scale,
nonlinear systems. Building on recently developed equation-free methods, this
approach numerically integrates the closure dynamics, the equations of which
may not even be available in closed form. Although closure dynamics introduce
statistical assumptions of unknown validity, they can have significant
computational advantages as they typically have fewer degrees of freedom and
may be much less stiff than the original detailed model. The closure method can
in principle be applied to a wide class of nonlinear problems, including
strongly-coupled systems (either deterministic or stochastic) for which there
may be no scale separation. We demonstrate the equation-free approach for
implementing entropy-based Eyink-Levermore closures on a nonlinear stochastic
partial differential equation.
