Lagrangian averaged stochastic advection by Lie transport for fluids
Theodore D. Drivas, Darryl D. Holm, James-Michael Leahy

TL;DR
This paper introduces LA SALT, a novel stochastic PDE framework for ideal fluids that incorporates non-locality in probability space, leading to a regularized, closed-form expectation equation related to Navier-Stokes dynamics.
Contribution
It formulates the LA SALT equations, extending SALT by including non-local probability effects and advected quantities, providing a new regularization mechanism for fluid models.
Findings
LA SALT equations recover SALT equations before averaging.
The expectation field satisfies a Navier-Stokes-like equation with Lie-Laplacian dissipation.
The framework models the statistical evolution of fluctuations in stochastic fluid flows.
Abstract
We formulate a class of stochastic partial differential equations based on Kelvin's circulation theorem for ideal fluids. In these models, the velocity field is randomly transported by white-noise vector fields, as well as by its own average over realizations of this noise. We call these systems the Lagrangian averaged stochastic advection by Lie transport (LA SALT) equations. These equations are nonlinear and non-local, in both physical and probability space. Before taking this average, the equations recover the Stochastic Advection by Lie Transport (SALT) fluid equations introduced by Holm (2015). Remarkably, the introduction of the non-locality in probability space in the form of momentum transported by its own mean velocity gives rise to a closed equation for the expectation field which comprises Navier--Stokes equations with Lie--Laplacian "dissipation". As such, this form of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
