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# Communications in Mathematical Sciences

## Volume 15 (2017)

### Number 7

### Discrete-in-time random particle blob method for the Keller–Segel equation and convergence analysis

Pages: 1821 – 1842

DOI: http://dx.doi.org/10.4310/CMS.2017.v15.n7.a2

#### Authors

#### Abstract

We establish an error estimate of a discrete-in-time random particle blob method for the Keller–Segel (KS) equation in $\mathbb{R}^d (d \geq 2)$. With a blob size $\epsilon=N^{- \frac{1}{d(d+1)}} \log(N)$, we prove the convergence rate between the solution to the KS equation and the empirical measure of the random particle method under $L^2$ norm in probability, where $N$ is the number of the particles.

#### Keywords

coupling method, concentration inequality, splitting scheme, kernel density estimation, Newtonian aggregation, chemotaxis, Brownian motion, interacting particle system

#### 2010 Mathematics Subject Classification

35K55, 35Q92, 60H35, 65M12, 65M15, 65M75

Hui Huang is partially supported by the Alan Mekler Postdoctoral Fellowship in the Department of Mathematics at Simon Fraser University.

Jian-Guo Liu is partially supported by KI-Net NSF RNMS (Grant No. 1107444) and NSF DMS (Grant No. 1514826).

Paper received on 3 April 2016.

Paper accepted on 15 April 2017.