Sampling rare events across dynamical phase transitions
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American Institute of Physics
Non equilibrium systemsRare eventsLarge deviationsDynamical phase transitionsMonte Carlo methods
Chaos 29, 083106 (2019) [https://doi.org/10.1063/1.5091669]
SponsorshipMinisterio Español MINECO proyecto (No. FIS2017-84256-P). Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía and European Regional Development Fund (ERDF), ref. SOMM17/6105/UGR. C.P.E. acknowledges the funding received from the European Union’s Horizon 2020 re-search and innovation programme under the Marie Sklodowska-Curie Cofund Programme Athenea3I GrantAgreement No. 754446.
Interacting particle systems with many degrees of freedom may undergo phase transitions to sustain atypical fluctuations of dynamical observables such as the current or the activity. In some cases, this leads to symmetry-broken space-time trajectories which enhance the probability of such events due to the emergence of ordered structures. Despite their conceptual and practical importance, these dynamical phase transitions (DPTs) at the trajectory level are difficult to characterize due to the low probability of their occurrence. However, during the last decade, advanced computational techniques have been developed to measure rare events in simulations of many-particle systems that allow the direct observation and characterization of these DPTs. Here we review the application of a particular rare-event simulation technique, based on cloning Monte Carlo methods, to characterize DPTs in paradigmatic stochastic lattice gases. In particular, we describe in detail some tricks and tips of the trade, paying special attention to the measurement of order parameters capturing the physics of the different DPTs, as well as to the finite-size effects (both in the system size and in the number of clones) that affect the measurements. Overall, we provide a consistent picture of the phenomenology associated with DPTs and their measurement.