Global martingale solutions to a stochastic superquadratic Shigesada-Kawasaki-Teramoto type population system

  • Xi Lin Department of Mathematics and Physics, Guangzhou Maritime University, Guangzhou 510725, China
Article ID: 3619
Keywords: global martingale solutions, existence of solutions, Galerkin approximation, auxiliary sequence, tightness criterion, stochastic Shiegesada-Kawasaki-Teramoto (SKT) system, superquadratic cross-diffusion, population dynamics

Abstract

For a stochastic cross-diffusion population system with superquadratic transition rate, we show that a global martingale solution exists. The existence of a global martingale solution proof for a stochastic population system is quite different from the existence of a weak solution proof for a deterministic population system. For deterministic population systems, we apply the entropy method to show a weak solution exists. For stochastic population systems, we rely on the Galerkin approximation scheme to derive the sequence of approximated solutions. We apply the Itbo formula to derive uniform estimates. After the tightness property be proved based on the estimation, a space changing result then be used to confirm the limit is a martingale solution of the cross-diffusion system. In the uniform estimation process, we notice that we have to estimate stochastic processes that are not in the finite dimensional space, otherwise we can not derive strong enough estimation for the tightness proof. We are not able to apply the Itbo formula to stochastic processes that are not finitely dimensional processes. In this situation, we have to introduce an auxiliary sequence. The estimation of the approximated sequence has to be derived on the estimation of an auxiliary sequence, which is the key idea of this work. The nonnegative property for approximated solutions has been shown by the standard Stampacchia method.

Published
2025-09-30
How to Cite
Lin, X. (2025). Global martingale solutions to a stochastic superquadratic Shigesada-Kawasaki-Teramoto type population system. Advances in Differential Equations and Control Processes, 32(3). https://doi.org/10.59400/adecp3619
Section
Article

References

[1]Shigesada N, Kawasaki K, Teramoto E. Spatial segregation of interacting species. Journal of Theoretical Biology. 1979; 79(1): 83–99. doi: 10.1016/0022-5193(79)90258-3

[2]Dhariwal G, Jüngel A, Zamponi N. Global martingale solutions for a stochastic population cross-diffusion system. Stochastic Processes and their Applications. 2019; 129(10): 3792–3820. doi: 10.1016/j.spa.2018.11.001

[3]Braukhoff M, Huber F, Jüngel A. Global martingale solutions for stochastic Shigesada–Kawasaki–Teramoto population models. Stochastics and Partial Differential Equations: Analysis and Computations. 2024; 12(1): 525–575. doi: 10.1007/s40072-023-00289-7

[4]Dhariwal G, Huber F, Jüngel A, et al. Global martingale solutions for quasilinear SPDEs via the boundedness-by-entropy method. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques. 2021; 57(1). doi: 10.1214/20-AIHP1088

[5]Prevot C, R¨ockner M. A concise course on Stochastic partial differential equations. Springer; 2007.

[6]Krylov NV. On Kolmogorovs equations for finite dimensional diffusions. In Krylov NV, R¨ockner M, Zabczyk J (editors). Stochastic PDEs and Kolmogorov Equations in Infinite Dimensions (Cetraro, 1998). Springer; 1999. pp. 1–63.

[7]Chen L, Jüngel A. Analysis of a multidimensional parabolic population model with strong cross-diffusion. SIAM Journal on Mathematical Analysis. 2004; 36(1): 301–322. doi: 10.1137/S0036141003427798

[8]Chen L, Jüngel A. Analysis of a parabolic cross-diffusion population model without self-diffusion. Journal of Differential Equations. 2006; 224(1): 39–59. doi: 10.1016/j.jde.2005.08.002

[9]Chen X, Daus ES, Jüngel A. Global existence analysis of cross-diffusion population systems for multiple species. Archive for Rational Mechanics and Analysis. 2018; 227(2): 715–747. doi: 10.1007/s00205-017-1172-6

[10]Chen X, Jüngel A. Global renormalized solutions to reaction-cross-diffusion systems with self-diffusion. Journal of Differential Equations. 2019; 267(10): 5901–5937. doi: 10.1016/j.jde.2019.06.010

[11]Jüngel A. Entropy methods for diffusive partial differential equations. Springer; 2016.

[12]Jüngel A. The boundedness-by-entropy method for cross-diffusion systems. Nonlinearity. 2015; 28(6): 1963–2001. doi: 10.1088/0951-7715/28/6/1963

[13]Chen X, Jüngel A. Analysis of an Incompressible Navier–Stokes–Maxwell–Stefan System. Communications in Mathematical Physics. 2015; 340(2): 471–497. doi: 10.1007/s00220-015-2472-z

[14]Jüngel A, Stelzer IV. Existence analysis of maxwell--stefan systems for multicomponent mixtures. SIAM Journal on Mathematical Analysis. 2013; 45(4): 2421–2440. doi: 10.1137/120898164

[15]Chen X, Jüngel A, Lin X, et al. Large-time asymptotics for degenerate cross-diffusion population models with volume filling. Journal of Differential Equations. 2024; 386: 1–15. doi: 10.1016/j.jde.2023.12.017

[16]Gerstenmayer A, Jüngel A. Analysis of a degenerate parabolic cross-diffusion system for ion transport. Journal of Mathematical Analysis and Applications. 2018; 461(1): 523–543. doi: 10.1016/j.jmaa.2018.01.024

[17]Brze´zniak Z, Motyl E. The existence of martingale solutions to the stochastic Boussinesq equations. Global and Stochastic Analysis. 2014; 1 (2): 175–216. Available online: https://www.mukpublications.com/resources/gsa2no3.pdf

[18]Brze´zniak Z, Ondreját M, Stochastic wave equations with values in Riemannian manifolds. Stochastic partial differential equations and applications, Quaderni di Matematica. 2010; 25: 65–97. Available online: https://library.utia.cas.cz/separaty/2012/SI/ondrejat-stochastic%20wave%20equations%20with%20values%20in%20riemannian%20manifolds.pdf

[19]Jakubowski A. The almost sure Skorohod representation for subsequences in nonmetric spaces. Theory of Probability & Its Application. 1997; 42: 167–175. Available online: https://www.researchgate.net/publication/247754371_The_as_Skorohod_representation_for_subsequences_in_nonmetric_spaces

[20]Ikeda N, Watanabe S, Stochastic differential equations and diffusion processes, 2nd ed. North- Holland; 1989.

[21]Karatzas I, Shreve S, Brownian motion and Stochastic calculus. In: Graduate Texts Math. Springer; 1988. Available online: https://personal.ntu.edu.sg/nprivault/MA5182/brownian-motion-stochastic-calculus.pdf

[22]Chekroun MD, Park E, Temam R. The Stampacchia maximum principle for stochastic partial differential equations and applications. Journal of Differential Equations. 2016; 260(3): 2926–2972. doi: 10.1016/j.jde.2015.10.022

[23]Brezis H. Functional analysis, Sobolev spaces and partial differential equations. Springer; 2011.

[24]Dubinskiyl YA. Weak convergence for nonlinear elliptic and parabolic equations. Mat. Sb. 1965; 67(109): 609–642 (in Russian).