The Annals of Probability

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

Gautam Iyer and Alexei Novikov
Source: Ann. Probab. Volume 39, Number 4 (2011), 1468-1501.

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 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 1/N ∑1N 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 ≥ 2, and consequently as N → ∞ we get convergence to the solution of the viscous Burgers equation on long time intervals.

First Page: Show Hide
Primary Subjects: 60H15
Secondary Subjects: 65C35, 35L67
Full-text: Access denied (no subscription detected)
We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber.
If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.aop/1312555805
Digital Object Identifier: doi:10.1214/10-AOP586
Zentralblatt MATH identifier: 05955099
Mathematical Reviews number (MathSciNet): MR2857247

References

[1] Alibaud, N., Droniou, J. and Vovelle, J. (2007). Occurrence and non-appearance of shocks in fractal Burgers equations. J. Hyperbolic Differ. Equ. 4 479–499.
Mathematical Reviews (MathSciNet): MR2339805
Digital Object Identifier: doi:10.1142/S0219891607001227
[2] Beale, J. T., Kato, T. and Majda, A. (1984). Remarks on the breakdown of smooth solutions for the 3-D Euler equations. Comm. Math. Phys. 94 61–66.
Mathematical Reviews (MathSciNet): MR763762
Zentralblatt MATH: 0573.76029
Digital Object Identifier: doi:10.1007/BF01212349
Project Euclid: euclid.cmp/1103941230
[3] Constantin, P. and Iyer, G. (2008). A stochastic Lagrangian representation of the three-dimensional incompressible Navier–Stokes equations. Comm. Pure Appl. Math. 61 330–345.
Mathematical Reviews (MathSciNet): MR2376844
Zentralblatt MATH: 1156.60048
Digital Object Identifier: doi:10.1002/cpa.20192
[4] De Lellis, C., Otto, F. and Westdickenberg, M. (2004). Minimal entropy conditions for Burgers equation. Quart. Appl. Math. 62 687–700.
Mathematical Reviews (MathSciNet): MR2104269
[5] Evans, L. C. (1998). Partial Differential Equations. Graduate Studies in Mathematics 19. Amer. Math. Soc., Providence, RI.
Mathematical Reviews (MathSciNet): MR1625845
[6] Fathi, A. (1997). Théorème KAM faible et théorie de Mather sur les systèmes lagrangiens. C. R. Acad. Sci. Paris Sér. I Math. 324 1043–1046.
Mathematical Reviews (MathSciNet): MR1451248
Digital Object Identifier: doi:10.1016/S0764-4442(97)87883-4
[7] Iyer, G. (2006). A stochastic perturbation of inviscid flows. Comm. Math. Phys. 266 631–645.
Mathematical Reviews (MathSciNet): MR2238892
Zentralblatt MATH: 1127.76017
Digital Object Identifier: doi:10.1007/s00220-006-0058-5
[8] Iyer, G. (2006). A stochastic Lagrangian formulation of the Navier–Stokes and related transport equations. Ph.D. thesis, Univ. Chicago.
[9] Iyer, G. and Mattingly, J. (2008). A stochastic-Lagrangian particle system for the Navier–Stokes equations. Nonlinearity 21 2537–2553.
Mathematical Reviews (MathSciNet): MR2448230
Zentralblatt MATH: 1158.60383
Digital Object Identifier: doi:10.1088/0951-7715/21/11/004
[10] Jauslin, H. R., Kreiss, H. O. and Moser, J. (1999). On the forced Burgers equation with periodic boundary conditions. In Differential Equations: La Pietra 1996 (Florence). Proc. Sympos. Pure Math. 65 133–153. Amer. Math. Soc., Providence, RI.
Mathematical Reviews (MathSciNet): MR1662751
Zentralblatt MATH: 0930.35156
[11] Karatzas, I. and Shreve, S. E. (1991). Brownian Motion and Stochastic Calculus, 2nd ed. Graduate Texts in Mathematics 113. Springer, New York.
Mathematical Reviews (MathSciNet): MR1121940
[12] Kiselev, A., Nazarov, F. and Shterenberg, R. (2008). Blow up and regularity for fractal Burgers equation. Dyn. Partial Differ. Equ. 5 211–240.
Mathematical Reviews (MathSciNet): MR2455893
Zentralblatt MATH: 1186.35020
[13] Krylov, N. V. (1999). An analytic approach to SPDEs. In Stochastic Partial Differential Equations: Six Perspectives. Math. Surveys Monogr. 64 185–242. Amer. Math. Soc., Providence, RI.
Mathematical Reviews (MathSciNet): MR1661766
Zentralblatt MATH: 0933.60073
[14] Krylov, N. V. and Rozovskiĭ, B. L. (1982). Stochastic partial differential equations and diffusion processes. Uspekhi Mat. Nauk 37 75–95.
Mathematical Reviews (MathSciNet): MR683274
[15] Krylov, N. V. (2007). Maximum principle for SPDEs and its applications. In Stochastic Differential Equations: Theory and Applications. Interdiscip. Math. Sci. 2 311–338. World Sci. Publ., Hackensack, NJ.
Mathematical Reviews (MathSciNet): MR2393582
Zentralblatt MATH: 1135.60039
[16] Kunita, H. (1990). Stochastic Flows and Stochastic Differential Equations. Cambridge Studies in Advanced Mathematics 24. Cambridge Univ. Press, Cambridge.
Mathematical Reviews (MathSciNet): MR1070361
[17] Majda, A. J. and Bertozzi, A. L. (2002). Vorticity and Incompressible Flow. Cambridge Texts in Applied Mathematics 27. Cambridge Univ. Press, Cambridge.
Mathematical Reviews (MathSciNet): MR1867882
[18] Metropolis, N. and Ulam, S. (1949). The Monte Carlo method. J. Amer. Statist. Assoc. 44 335–341.
Mathematical Reviews (MathSciNet): MR31341
Zentralblatt MATH: 0033.28807
Digital Object Identifier: doi:10.2307/2280232
[19] Robert, C. P. and Casella, G. (2004). Monte Carlo Statistical Methods, 2nd ed. Springer, New York.
Mathematical Reviews (MathSciNet): MR2080278
[20] Rozovskiĭ, B. L. (1990). Stochastic Evolution Systems. Linear Theory and Applications to Nonlinear Filtering. Translated from the Russian by A. Yarkho. Mathematics and Its Applications (Soviet Series) 35. Kluwer, Dordrecht.
Mathematical Reviews (MathSciNet): MR1135324
[21] Weinan, E., Khanin, K., Mazel, A. and Sinai, Y. (2000). Invariant measures for Burgers equation with stochastic forcing. Ann. of Math. (2) 151 877–960.
Mathematical Reviews (MathSciNet): MR1779561
Zentralblatt MATH: 0972.35196
Digital Object Identifier: doi:10.2307/121126
[22] Yudovich, V. I. (1963). Non-stationary flows of an ideal incompressible fluid. Z. Vychisl. Mat. i Mat. Fiz. 3 1032–1066.
Mathematical Reviews (MathSciNet): MR158189

2013 © Institute of Mathematical Statistics

The Annals of Probability

The Annals of Probability

Turn MathJax Off
What is MathJax?