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Chenlin Gu

Publications and source records attributed to Chenlin Gu.

21 records · Page 2Linked to original sources

Quantitative homogenization of the parabolic and elliptic Green's functions on percolation clusters

We study the heat kernel and the Green's function on the infinite supercritical percolation cluster in dimension $d \geq 2$ and prove a quantitative homogenization theorem for these functions with an almost optimal rate of convergence. These results are a quantitative version of the local central limit theorem proved by Barlow and Hambly. The proof relies on a structure of renormalization for the infinite percolation cluster introduced by Armstrong and the first author, Gaussian bounds on the heat kernel established by Barlow and tools of the theory of quantitative stochastic homogenization. An important step in the proof is to establish a $C^{0,1}$-large-scale regularity theory for caloric functions on the infinite cluster and is of independent interest.

math.PR↗

Decay of semigroup for an infinite interacting particle system on continuum configuration spaces

We show the heat kernel type variance decay $t^{-\frac{d}{2}}$, up to a logarithmic correction, for the semigroup of an infinite particle system on $\mathbb{R}^d$, where every particle evolves following a divergence-form operator with diffusivity coefficient that depends on the local configuration of particles. The proof relies on the strategy from $\mathbb{Z}^d$ zero range model, and generalizes the localization estimate to the continuum configuration space introduced by S. Albeverio, Y.G. Kondratiev and M. Röckner.

math.PR↗

An efficient algorithm for solving elliptic problems on percolation clusters

We present an efficient algorithm to solve elliptic Dirichlet problems defined on the cluster of $\mathbb{Z}^d$ supercritical Bernoulli percolation, as a generalization of the iterative method proposed by S. Armstrong, A. Hannukainen, T. Kuusi and J.-C. Mourrat. We also explore the two-scale expansion on the infinite cluster of percolation, and use it to give a rigorous analysis of the algorithm.

math.PR↗