Using Sequential Importance Sampling for Nonequilibrium Reweighting

Abstract

We present a reweighting method for nonequilibrium Markov processes. With nonequilibrium Monte Carlo simulations at one temperature, one calculates the averages of time dependent physical quantities for a range of temperatures. We propose that reweighting for nonequilibrium is most useful when combined with dynamical finite size scaling. The procedure is demonstrated for the Ising model and the driven diffusive lattice gas model in this paper.