Concentration Phenomenon in Some Non-Local Equation
Olivier Bonnefon (BIOSP), J\'er\^ome Coville (BIOSP), Guillaume, Legendre (CEREMADE)

TL;DR
This paper studies the long-term behavior of solutions to a non-local integro-differential equation modeling population dynamics, showing existence, uniqueness, and convergence to stationary measures, with numerical exploration of singular and non-unique cases.
Contribution
It constructs stationary solutions as Radon measures, proves their uniqueness under regularity, and analyzes convergence of solutions, including numerical investigation of singular and non-unique measures.
Findings
Existence of stationary Radon measure solutions.
Uniqueness of regular and bounded stationary measures.
Numerical evidence of dependence on initial data in singular cases.
Abstract
We are interested in the long time behaviour of the positive solutions of the Cauchy problem involving the following integro-differential equation \partial\_t u(t, x) = \left(a(x) -- \int\_{\Omega} k(x, y)u(t, y) dy\right ) u(t, x) + \int\_{\Omega} m(x, y)[u(t, y) -- u(t, x)] dy\quad \text{ for}\quad (t, x) $\in$ \mathbb{R}\_{+} \times \Omega, together with the initial condition . Such a problem is used in population dynamics models to capture the evolution of a clonal population structured with respect to a phenotypic trait. In this context, the function u represents the density of individuals characterized by the trait, the domain of trait values is a bounded subset of , the kernels and respectively account for the competition between individuals and the mutations occurring in every generation, and the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolution and Genetic Dynamics · Mathematical Biology Tumor Growth · Mathematical and Theoretical Epidemiology and Ecology Models
