BPCPPDYSOE21 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 32: Posters DY - Statistical Physics, Brownian Motion and Nonlinear Dynamics
DY 32.16: Poster
Dienstag, 23. März 2021, 16:30–19:00, DYp
The Role of Resampling in Population Annealing — •Denis Gessert1,2 and Martin Weigel1,3 — 1Applied Mathematics Research Centre, Coventry University, Coventry, CV1 5FB, United Kingdom — 2Institut für Theoretische Physik, Leipzig University, Postfach 100920, D-04009 Leipzig, Germany — 3Institut für Physik, Technische Universität Chemnitz, D-09107 Chemnitz, Germany
Population Annealing (PA) is a population-based Monte Carlo algorithm that can be used for equilibrium simulations of thermodynamic systems with a rough free energy landscape. The algorithm has a number of parameters that can be fine-tuned to improve performance. While there is some theoretical and numerical work relating the parameters, little is known to date about the effect of choosing specific resampling protocols.
The 2d Ising model is used as a benchmarking system for this study. At first various resampling methods are implemented and numerically compared using a PA implementation on GPUs. In a second part the exact solution of the Ising model is utilized to create an artificial PA setting with effectively infinite Monte Carlo updates at each temperature as well as an infinite population. This allows one to look at resampling in isolation from other parameters and draw some general conclusions about the effects of the choice of resampling scheme.