| Co-Investigator(Kenkyū-buntansha) |
HIROSE Munemitsu Meiji Univ., School of Sci. & Tech., Lecturer, 理工学部, 専任講師 (50287984)
TANAKA Kazunaga School of Science and Engineering, Professor, 理工学部, 教授 (20188288)
OTANI Mitsuharu School of Science and Engineering, Professor, 理工学部, 教授 (30119656)
TAKEUCHI Shingo Gakushuin Univ., Dept. of Math., Assistant Professor, 理学部, 助手 (00333021)
NAKASHIMA Kimie Tokyo Univ. of Fisheries, Associate Professor, 助教授 (10318800)
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| Research Abstract |
In our project we have mainly discussed the stationary and non-stationary problems for the following reaction diffusion systems with quasilinear diffusion terms:
(E) u_l = Δ[(1 + αv + γu)u] + uf (u, v), v_l = Δ[(1 + βv + δv)v] + vg (u, v).
This is a well-known system which models the habitat segregation phenomenon between two species. In (E) u, v denote the population densities and f, g represent the interaction between u and v such as Lotka-Volterra competition type or prey-predator type.
(1) Non-stationary problem. When the system has a cross-diffusion effect, the existence result of global solutions was restricted to the two dimensional case. We have proved that, if α, γ > 0 and β = δ = 0, then (E) admits a unique global solution without any restrictions on the space dimension and the amplitude of initial data. Our strategy is to decouple the system and study reaction-diffusion equations separately. We combine parabolic fundamental estimates with energy estimates of solutions of parabolic equation with self-diffusion. This method is also valid for the case δ > 0; so that the global existence is shown when the space dimension is less than six.
(2) Stationary problem. From the view-point of mathematical biology, it is very important to study positive stationary solutions and to know their number. We have tried to get some conditions for the multiplicity of such positive solutions. In particular, the multiple existence is established if interactions are very large in case of competition model with linear diffusion or if one of cross-diffusion is very large in case of prey-predator model.
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