The regularizing effects of resetting in a particle system for the Burgers equation

TL;DR

This paper investigates a stochastic particle system for the viscous Burgers equation, showing that resetting procedures prevent shocks and enable convergence to the true solution over long times.

Contribution

The study introduces a resetting mechanism in a Monte Carlo particle system for Burgers equations, preventing shocks and ensuring convergence to the viscous solution.

Findings

Finite N particle system shocks almost surely in finite time.

Resetting procedure prevents shock formation for all N ≥ 2.

As N approaches infinity, the system converges to the viscous Burgers solution.

Abstract

We study the dissipation mechanism of a stochastic particle system for the Burgers equation. The velocity field of the viscous Burgers and Navier-Stokes equations can be expressed as an expected value of a stochastic process based on noisy particle trajectories [Constantin and Iyer Comm. Pure Appl. Math. 3 (2008) 330-345]. In this paper we study a particle system for the viscous Burgers equations using a Monte-Carlo version of the above; we consider N copies of the above stochastic flow, each driven by independent Wiener processes, and replace the expected value with $\frac{1}{N}$ times the sum over these copies. A similar construction for the Navier-Stokes equations was studied by Mattingly and the first author of this paper [Iyer and Mattingly Nonlinearity 21 (2008) 2537-2553]. Surprisingly, for any finite N, the particle system for the Burgers equations shocks almost surely in finite time. In contrast to the full expected value, the empirical mean $\frac{1}{N}\sum_1^N$ does not regularize the system enough to ensure a time global solution. To avoid these shocks, we consider a resetting procedure, which at first sight should have no regularizing effect at all. However, we prove that this procedure prevents the formation of shocks for any $N\geq2$, and consequently as $N\to\infty$ we get convergence to the solution of the viscous Burgers equation on long time intervals.