Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T02:23:37.345Z Has data issue: false hasContentIssue false

On gradients of approximate travelling waves for generalised KPP equations

Published online by Cambridge University Press:  14 November 2011

H. Z. Zhao
Affiliation:
Department of Mathematics, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, U.K.

Abstract

In this paper we use stochastic semiclassical analysis and the logarithmic transformation to study the gradients of the approximate travelling wave solutions for the generalised KPP equations with Gaussian and Dirac delta initial distributions. We apply the logarithmic transformation to the nonlinear reaction diffusion equations and obtain a Maruyama–Girsanov–Cameron–Martin formula for the drift μ2 log uμ, uμ being a solution of a generalised KPP equation. We obtain that μ2|∇ log uμ(t,x)| is bounded and the trough is flat. The difficult problem in this paper is to prove that the corresponding crest is flat. A probabilistic approach is used in this paper to treat this problem successfully.

Type
Research Article
Copyright
Copyright © Royal Society of Edinburgh 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1Champneys, A., Harris, S., Toland, J., Warren, J. and Williams, D.. Algebra, analysis and probability for a coupled system of reaction-diffusion equations. Philos. Trans. Roy. Soc. London 305 (1995), 69112.Google Scholar
2Elworthy, K. D. and Truman, A.. The diffusion equation and classical mechanics: an elementary formula. In Stochastic Processes in Quantum Physics, eds Albeverio, S. et al. Lecture Notes in Physics 173, 136–46 (Berlin: Springer, 1982).CrossRefGoogle Scholar
3Elworthy, K. D.. Stochastic flows on Riemannian manifolds. In Diffusion Processes and Related Problems in Analysis, Vol. II, Stochastic Flows, eds Pinsky, M. and Wihstutz, V., 3772 (Boston: Birkhauser, 1992).Google Scholar
4Elworthy, K. D., Truman, A., Zhao, H. Z. and Gaines, J. G.. Approximate travelling waves for the generalized KPP equations and classical mechanics. Proc. Roy. Soc. London Ser. A 446 (1994), 529–54.Google Scholar
5Elworthy, K. D. and Li, X.-M.. Formulae for the derivatives of heat semigroups. J. Fund. Anal. 125 (1994), 252–86.CrossRefGoogle Scholar
6Evans, L. C- and Sougandis, P. E.. A PDE approach to geometric optics for certain semilinear parabolic equations. Indiana Univ. Math. J. 38 (1989), 141–72.CrossRefGoogle Scholar
7Fleming, W. H.. Controlled Markov processes and viscosity solution of nonlinear evolution equations (Lezioni Fermiane, Scuola Normale Superiore di Pisa, 1986).Google Scholar
8Fleming, W. H. and Soner, H. M.. Controlled Markov Processes and Viscosity Solutions (New York: Springer, 1993).Google Scholar
9Freidlin, M. I.. Functional Integration and Partial Differential Equations (Princeton, NJ: Princeton University Press, 1985)Google Scholar
10Freidlin, M. I.. Semilinear PDEs and Limit Theorem for Large Deviations, Lecture Notes in Mathematics 1527, 1109 (Berlin: Springer, 1992).Google Scholar
11Freidlin, M. I.. Some general properties of evolution processes quasi-deterministic approximation. In Proceedings of the VIIIth International Congress on Mathematical Physics, eds Mebkhout, M. and Seneor, R., 470–81 (Singapore: World Scientific, 1987).Google Scholar
12Hopf, E.. The partial differential equation ut + uux = μuxx. Comm. Pure Appl. Math. 3 (1950), 201–30.CrossRefGoogle Scholar
13Karpelevich, F. I., Kelbert, M. Ya and Suhov, Yu. M.. The branching diffusion, stochastic equations and travelling wave solutions to Kolmogoroff–Petrovskii–Piskunoff. In Cellular Automata and Cooperative Systems, eds. Boccara, N., Goles, E., Martinez, S. and Picco, P., 333–66 (Dordrecht: Kluwer, 1993).Google Scholar
14Kolmogoroff, A., Petrovsky, I. and Piscounoff, N.. Study of the diffusion equation with growth of the quantity of matter and its application to a biology problem. Moscow Univ. Bull. Math. 1 (1937), 125. (English translation: in Dynamics of Curved Front, ed. Pierre Pelce, 105–30 (New York: Academic Press, 1988).)Google Scholar
15Li, X.-M. and Zhao, H. Z.. The smooth convergence of the solutions of reaction diffusion equations with small parameter. Nonlinearity 9 (1996), 459–77.CrossRefGoogle Scholar
16McKean, H. P.. Application of Brownian motion to the equation of Kolmogoroff–Petrovskii–Piskunoff. Comm. Pure Appl. Math. 28 (1975), 323–31.CrossRefGoogle Scholar
17Sheu, S.-J.. Some estimates of the transition density of a nondegenerate diffusion Markov process (Preprint, 1994).Google Scholar
18Truman, A. and Zhao, H. Z.. On stochastic diffusion equations and stochastic Burgers' equations. J. Math. Phys. 37 (1996), 283307.CrossRefGoogle Scholar
19Williams, D., Truman, A. and Zhao, H. Z.. Notes on Fisher–Kolmogorov–Petrovskii–Piskunov equations, I (in preparation).Google Scholar
20Zhao, H. Z. and Elworthy, K. D.. The travelling wave solutions of scalar generalized KPP equations via classical mechanics and stochastic approaches. In Stochastics and Quantum Mechanics, eds Truman, A. and Davies, I. M., 298316 (Singapore: World Scientific, 1992).Google Scholar