Computing diffusivities from particle models out of equilibrium
Abstract
A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have Gaussian fluctuations but it is otherwise allowed to undergo arbitrary outofequilibrium evolutions. This could be potentially relevant for particle data obtained from experimental applications. The key idea underlying the method is that finite, yet large, particle systems formally obey stochastic partial differential equations of gradient flow type satisfying a fluctuationdissipation relation. The strategy is here applied to three classic particle models, namely independent random walkers, a zerorange process and a symmetric simple exclusion process in one space dimension, to allow the comparison with analytic solutions.
 Publication:

Proceedings of the Royal Society of London Series A
 Pub Date:
 April 2018
 DOI:
 10.1098/rspa.2017.0694
 arXiv:
 arXiv:1710.03680
 Bibcode:
 2018RSPSA.47470694E
 Keywords:

 Condensed Matter  Statistical Mechanics
 EPrint:
 doi:10.1098/rspa.2017.0694