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  Efficient characterisation of large deviations using population dynamics

Brewer, T., Clark, S. R., Bradford, R., & Jack, R. L. (2018). Efficient characterisation of large deviations using population dynamics. Journal of Statistical Mechanics: Theory and Experiment, 053204. doi:10.1088/1742-5468/aab3ef.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-A7BC-F Version Permalink: http://hdl.handle.net/21.11116/0000-0002-501E-3
Genre: Journal Article

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https://dx.doi.org/10.1088/1742-5468/aab3ef (Publisher version)
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 Creators:
Brewer, T.1, Author
Clark, S. R.1, 2, Author              
Bradford, R.3, Author
Jack, R. L.1, 4, 5, Author
Affiliations:
1Department of Physics, University of Bath, ou_persistent22              
2Quantum Condensed Matter Dynamics, Condensed Matter Dynamics Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_1938293              
3Department of Computer Science, University of Bath, ou_persistent22              
4Department of Applied Mathematics and Theoretical Physics, University of Cambridge, ou_persistent22              
5Department of Chemistry, University of Cambridge, ou_persistent22              

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Free keywords: large deviations in non-equilibrium systems, driven diffusive systems, exclusion processes
 Abstract: We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms.

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Language(s): eng - English
 Dates: 2017-11-142018-02-232018-05-08
 Publication Status: Published online
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1088/1742-5468/aab3ef
arXiv: 1711.05095
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Project name : RLJ thanks Takahiro Nemoto and Vivien Lecomte for useful discussions about the population dynamics algorithm. We also thank the anonymous referees for insightful comments and suggestions. Computations were performed on the HPC service at the University of Bath. TB’s PhD studentship is supported by ClusterVision.
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Title: Journal of Statistical Mechanics: Theory and Experiment
Source Genre: Journal
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Publ. Info: Bristol, England : Institute of Physics Publishing
Pages: - Volume / Issue: - Sequence Number: 053204 Start / End Page: - Identifier: ISSN: 1742-5468
CoNE: https://pure.mpg.de/cone/journals/resource/111076098244006