Generalized-ensemble population annealing simulations

ORAL  · Invited

Abstract

Population annealing is a modern and flexible set of techniques that combines strong performance for systems with complex free-energy landscapes with excellent scalability on massively parallel computational architectures. In this presentation we provide a compact introduction to the method and some of the most important avenues for improving its performance, including adaptive annealing protocols, time stepping and population sizes. After showcasing a range of incarnations ranging from population annealing molecular dynamics to quantum population annealing, we introduce and discuss population Monte Carlo schemes for simulating systems in generalized ensembles, including microcanonical and multicanonical variants.

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Publication: [1] L. Yu. Barash, M. Weigel, M. Borovský, W. Janke, and L. N. Shchur, GPU accelerated population annealing algorithm, Comput. Phys. Commun. 220, 341 (2017).

[2] L. Y. Barash, M. Weigel, L. N Shchur, and W. Janke, Exploring first-order phase transitions with population annealing, Eur. Phys. J. Special Topics 226, 595 (2017).

[3] L. Barash, J. Marshall, M. Weigel, and I. Hen, Estimating the Density of States of Frustrated Spin Systems, New J. Phys. 21, 073065 (2019).

[4] M. Weigel, L. Barash, L. Shchur, and W. Janke, Understanding population annealing Monte Carlo simulations, Phys. Rev. E 103, 053301 (2021).

[5] P. L. Ebert, D. Gessert, and M. Weigel, Weighted averages in population annealing: Analysis and general framework, Phys. Rev. E 106, 045303 (2022).

[6] D. Gessert, W. Janke, and M. Weigel, Resampling schemes in population annealing: Numerical and theoretical results, Phys. Rev. E 108, 065309 (2023).

Presenters

  • Martin Weigel

    • Tech Univ Chemnitz Zwickau

Authors

  • Martin Weigel

    • Tech Univ Chemnitz Zwickau