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Quantitative convergence in relative entropy for a moderately interacting particle system on mathbb{R}^d
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Quantitative convergence in relative entropy for a moderately interacting particle system on mathbb{R}^d
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This article shows how to combine the relative entropy method by D. Bresch, P.-E. Jabin, and Z. Wang in arXiv:1706.09564, arXiv:1906.04093 and the regularized $L^2(\mathbb{R}^d)$-estimate by Oelschl\"ager (Probability theory and related fields, 1987) to prove a strong propagation of chaos result for the viscous porous medium equation from a moderately interacting particle system in $L^\infty(0,T; L^1(\mathbb{R}^d))$-norm. In the moderate interacting setting, the interacting potential is a smoothed Dirac Delta distribution, however, current results regarding the relative entropy methods for singular potentials do not apply. The result holds on $\mathbb{R}^d$ for any dimension $d\geq 1$ and provides a quantitative result where the rate of convergence depends on the moderate scaling parameter and the dimension $d\geq 1$. Additionally, the presented method can be adapted for moderately interacting systems for which a certain convergence probability holds -- thus a propagation of chaos result in relative entropy can be obtained for kernels approximating Coulomb potentials.
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Cited by 1 Pith paper
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Density-Dependent McKean--Vlasov Diffusions: Subgaussian Occupancy Bounds and Polynomial Propagation of Chaos
A clipped, shifted-histogram particle system approximating density-dependent McKean–Vlasov diffusions has relative-entropy error O(N^{-2/(d+2)}(log N)^{d/(d+2)}) per particle over finite time.
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