Geometric ergodicity of a bead-spring pair with stochastic Stokes forcing
Abstract
We consider a simple model for the fluctuating hydrodynamics of a flexible polymer
in a dilute solution, demonstrating geometric ergodicity for a pair of particles that
interact with each other through a nonlinear spring potential while being advected
by a stochastic Stokes fluid velocity field. This is a generalization of previous
models which have used linear spring forces as well as white-in-time fluid velocity
fields. We follow previous work combining control theoretic arguments, Lyapunov functions,
and hypo-elliptic diffusion theory to prove exponential convergence via a Harris chain
argument. In addition we allow the possibility of excluding certain "bad" sets in
phase space in which the assumptions are violated but from which the system leaves
with a controllable probability. This allows for the treatment of singular drifts,
such as those derived from the Lennard-Jones potential, which is a novel feature of
this work. © 2012 Elsevier B.V. All rights reserved.
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https://hdl.handle.net/10161/9524Published Version (Please cite this version)
10.1016/j.spa.2012.07.003Publication Info
Mattingly, JC; McKinley, SA; & Pillai, NS (2012). Geometric ergodicity of a bead-spring pair with stochastic Stokes forcing. Stochastic Processes and their Applications, 122(12). pp. 3953-3979. 10.1016/j.spa.2012.07.003. Retrieved from https://hdl.handle.net/10161/9524.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Jonathan Christopher Mattingly
Kimberly J. Jenkins Distinguished University Professor of New Technologies
Jonathan Christopher Mattingly grew up in Charlotte, NC where he attended Irwin Ave
elementary and Charlotte Country Day. He graduated from the NC School of Science
and Mathematics and received a BS is Applied Mathematics with a concentration in physics
from Yale University. After two years abroad with a year spent at ENS Lyon studying
nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to
attend Princeton University where he obtained a PhD in Applied and

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