Sampling microcanonical measures of the 2D Euler equations through Creutz's algorithm: a phase transition from disorder to order when energy is increased
Max Potters, Timothee Vaillant, Freddy Bouchet

TL;DR
This paper demonstrates how Creutz's algorithm can sample microcanonical measures of the 2D Euler equations, revealing a novel phase transition from disorder to order as energy increases, and confirming theoretical predictions.
Contribution
It introduces the use of Creutz's algorithm for sampling 2D Euler microcanonical measures and uncovers a new phase transition from disorder to order with increasing energy.
Findings
Creutz's algorithm successfully samples 2D Euler microcanonical measures.
A first-order phase transition from disordered to ordered states is observed.
The results confirm the mean-field statistical mechanics theory predictions.
Abstract
The 2D Euler equations is the basic example of fluid models for which a microcanical measure can be constructed from first principles. This measure is defined through finite-dimensional approximations and a limiting procedure. Creutz's algorithm is a microcanonical generalization of the Metropolis-Hasting algorithm (to sample Gibbs measures, in the canonical ensemble). We prove that Creutz's algorithm can sample finite-dimensional approximations of the 2D Euler microcanonical measures (incorporating fixed energy and other invariants). This is essential as microcanonical and canonical measures are known to be inequivalent at some values of energy and vorticity distribution. Creutz's algorithm is used to check predictions from the mean-field statistical mechanics theory of the 2D Euler equations (the Robert-Sommeria-Miller theory). We found full agreement with theory. Three different ways…
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