Lawrence Berkeley National Laboratory

We present a comparative study of two methods for the reduction of the dimensionality of a system of ordinary differential equations that exhibits time-scale separation. Both methods lead to a reduced system of stochastic differential equations. The novel feature of these methods is that they allow the use, in the reduced system, of higher order terms in the resolved variables. The first method, proposed by Majda, Timofeyev and Vanden-Eijnden, is based on an asymptotic strategy developed by Kurtz. The second method is a short-memory approximation of the Mori-Zwanzig projection formalism of irreversible statistical mechanics, as proposed by Chorin, Hald and Kupferman. We present conditions under which the reduced models arising from the two methods should have similar predictive ability. We apply the two methods to test cases that satisfy these conditions. The form of the reduced models and the numerical simulations show that the two methods have similar predictive ability as expected.