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Quantitative approximation of the Burgers and Keller-Segel equations by moderately interacting particles

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arxiv 2004.03177 v4 pith:NRZZOT3Q submitted 2020-04-07 math.PR

classification math.PR
keywords convergenceburgersempiricalkeller-segelmeasureobtainparticlesrate
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abstract

In this work we obtain rates of convergence for two moderately interacting stochastic particle systems with singular kernels associated to the viscous Burgers and Keller-Segel equations. The main novelty of this work is to consider a non-locally integrable kernel. Namely for the viscous Burgers equation in $\mathbb{R}$, we obtain almost sure convergence of the mollified empirical measure to the solution of the PDE in some Bessel space with a rate of convergence of order $N^{-1/6}$, on any time interval. With the same rate, convergence also holds for the genuine empirical measure in Wasserstein distance, and at the level of the trajectories of the particles with the standard coupling to McKean-Vlasov particles. In the case of the Keller-Segel equation on a $d$-dimensional torus, we obtain almost sure convergence of the mollified empirical measure to the solution of the PDE in some $L^q$ space with a rate of order $N^{-\frac{1}{2(d+1)}}$. The result holds up to the maximal existence time of the PDE, for any value of the chemo-attractant sensitivity $\chi$.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Propagation of chaos for multi-species moderately interacting particle systems up to Newtonian singularity

    math.PR 2025-01 accept novelty 8.0 of 10

    Multi-species moderately interacting particle systems converge in L1 to aggregation-diffusion PDEs with Coulomb-singular kernels at an algebraic rate in the particle number.

  2. McKean-Vlasov equations with singular coefficients - a review of recent results

    math.PR 2025-07 accept novelty 3.0 of 10

    This paper is a structured review of singular McKean-Vlasov SDEs, unifying the Lp-Lq and distributional drift frameworks and their main solution tools.

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