REVIEW 2 cited by
Mean-field limit of a particle approximation for the parabolic-parabolic Keller-Segel model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Mean-field limit of a particle approximation for the parabolic-parabolic Keller-Segel model
read the original abstract
In this paper, we study propagation of chaos for the parabolic-parabolic Keller-Segel model with a logarithmic cut-off by establishing a rigorous convergence analysis from a stochastic particle system to the parabolic-parabolic Keller-Segel (KS) equation for any dimension case. Under the assumption that the initial data are independent and identically distributed (i.i.d.) with a common probability density function $\rho_0$, we rigorously prove the propagation of chaos for this interacting system with a cut-off parameter $\varepsilon\sim (\ln N)^{-\frac{2}{d+2}}$: when $N\rightarrow \infty$, the joint distribution of the particle system is $f$-chaotic and the measure $f$ possesses a density which is a weak solution to the mean-field parabolic-parabolic KS equation.
Forward citations
Cited by 2 Pith papers
-
A Novel Stochastic Particle-Field Algorithm for a Reaction-Diffusion-Advection Cancer Invasion Model
A stochastic particle-field algorithm solves a 3D cancer invasion PDE system with bounded mass change, unconditional positivity, and proven convergence rates, claimed as the first such 3D solver.
-
A Novel Stochastic Particle-Field Algorithm for a Reaction-Diffusion-Advection Cancer Invasion Model
A stochastic particle-field algorithm solves a 3D reaction-diffusion-advection cancer invasion model while preserving positivity and providing error analysis, claimed as the first such 3D solution.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.