Institute of Mathematical Statistics Collections

From Particles with Random Potential to a Nonlinear Vlasov–Fokker–Planck Equation

Jin Feng

Abstract

We consider large time and infinite particle limit for a system of particles living in random potentials. The randomness enters the potential through an external ergodic Markov process, modeling oscillating environment with good statistical averaging properties. From each individual particle’s point of view, both law of large number and central limit theorem type of averaging are possible. Problems of this type have been well studied and are known as random evolutions. Instead of one particle, we focus on the collective behavior of infinite particles. We separately rescale potential functions (type one) which annihilates the equilibrium measure of the ergodic environment process, and the potential functions which may not annihilate such measure (type two). Appropriately rescaled to the macroscopic limit, type two potentials give a transport term while type one potentials give a nonlinear diffusion term. The resulting equation is a version of nonlinear Vlasov–Fokker–Planck equation. We will also prove the uniqueness of solution for such equation.

First Page: Show Hide
Full-text: Open access
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.imsc/1233152935 Digital Object Identifier: doi:10.1214/074921708000000309

References

[1] Bensoussan, Alain, Lions, Jacques-Louis and Papanicolaou, George (1978). Asymptotic Analysis for Periodic Structures. Studies in Mathematics and its Applications 5. North-Holland Publishing Co., Amsterdam, New York.
Mathematical Reviews (MathSciNet): MR503330
Zentralblatt MATH: 0404.35001
[2] Cercignani, C. (1988). The Boltzmann Equation and Its Applications. Applied Mathematical Sciences 67. Springer-Verlag, New York.
Mathematical Reviews (MathSciNet): MR1313028
Zentralblatt MATH: 0646.76001
[3] Dawson, Donald A. and Gärtner, Jürgen (1987). Large deviations from the McKean–Vlasov limit for weakly interacting diffusions. Stochastics 20 247–308.
[4] Ethier, Stewart N. and Kurtz, Thomas G. (1986). Markov Processes: Characterization and Convergence. John Wiley and Sons, New York.
Mathematical Reviews (MathSciNet): MR838085
[5] Kurtz, Thomas G. (1973). Convergence of sequences of semigroups of nonlinear operators with an application to gas kinetics. Trans. Amer. Math. Soc. 186 259–272.
Mathematical Reviews (MathSciNet): MR336482
Zentralblatt MATH: 0275.47047
Digital Object Identifier: doi:10.1090/S0002-9947-1973-0336482-1
[6] Kurtz, Thomas G. (1998). Martingale problems for conditional distributions of Markov processes. Electronic Journal of Probability 3 1–29.
Mathematical Reviews (MathSciNet): MR1637085
Zentralblatt MATH: 0907.60065
[7] Kurtz, Thomas G. and Protter, Philip (1996). Limit theorems for solutions of stochastic equations II. CIME School in Probability, Springer Lecture Notes in Mathematics 1627 197–285.
[8] Méléard, Sylvie (1996). Asymptotic behaviour of some interacting particle systems; McKean–Vlasov and Boltzmann models. Ibid 42–95.
[9] Stroock, Daniel W. and Varadhan, S. R. S. (1979). Multidimensional Diffusion Processes. Springer, New York. Corrected Second Printing, 1997.
Mathematical Reviews (MathSciNet): MR532498

2012 © Institute of Mathematical Statistics

Institute of Mathematical Statistics Collections

Institute of Mathematical Statistics Collections