Open Collections

Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning

Banff International Research Station for Mathematical Innovation and Discovery

We discuss sampling methods based on variable temperature (simulated tempering). We show using large deviation theory (and following the technique of [Plattner et al, JCP, 2011]) that the most efficient approach in simulated tempering is to vary the temperature infinitely rapidly over a continuous range. In this limit, we can replace the equations of motion for the temperature by averaged equations, with a rescaling of the force in the equations of motion. We give a theoretical argument for the choice of the temperature weights as the reciprocal partition function, thereby relating simulated tempering to Wang-Landau sampling. Finally, we describe a self-consistent algorithm for simultaneously sampling the canonical ensemble and learning the weights during simulation. This talk describes joint work with Jianfeng Lu, Benedict Leimkuhler and Eric Vanden-Eijnden.

Mathematics; Statistics; Statistical mechanics, structure of matter; Statistical mechanics

video/mp4

Author affiliation: Univ. Edinburgh

2019-05-15

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70168

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/