A list-based algorithm for evaluation of large deviation functions
نویسندگان
چکیده
As the analog of the free energy for dynamical trajectories, the large deviation function plays a central role in the statistical mechanics of systems far from equilibrium. Here, we identify numerical issues that can arise when the model of interest evolves according to a continuous-time dynamics. This analysis motivates the introduction of an algorithm in which a list of previously visited states is used to resample the distribution of interest. We discuss the convergence properties of our algorithm in detail and demonstrate its application to the singlesite zero-range process and the many-site totally asymmetric exclusion process.
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تاریخ انتشار 2010