论文标题

随机步行在点过程中的随机均匀化

Stochastic homogenization of random walks on point processes

论文作者

Faggionato, Alessandra

论文摘要

我们考虑在$ \ mathbb {r}^d $的随机纯原子测度的支持下随机步行,并随机跳跃概率率。跳跃范围可能是无限的。纯原子测度对于随机步行和固定的固定度是可逆的,用于组$ \ mathbb {g} = \ mathbb {r}^d $或$ \ mathbb {g} = \ mathbb {z}^d $。通过结合$ \ mathbb {g} $的两尺度收敛和棕榈理论 - 固定的随机度量并制定截止程序,在适当的第二瞬间条件下,我们几乎在所有环境中都证明了相关Markov Generator的大型泊松方程的均匀化。此外,我们还获得了$ l^2 $ markov semigroup的淬火收敛性,并在扩散的随机步行中分辨出与协方差矩阵$ 2D $的相应的随机步行。对于对称跳跃速率,上述收敛性在考虑使用位点排除或零范围相互作用的多个随机步行时,在流体动力限制的推导中起着至关重要的作用。我们不需要任何椭圆度假设,也不需要均质化矩阵$ d $的非分类。我们的结果涵盖了包括例如$ \ mathbb {z}^d $以及一般晶格上的随机电导模型(可能具有长时间电导率),Mott可变范围跳跃,在Delaunay三角形上进行简单的随机步行,在超临界渗透群中进行简单的随机步行。

We consider random walks on the support of a random purely atomic measure on $\mathbb{R}^d$ with random jump probability rates. The jump range can be unbounded. The purely atomic measure is reversible for the random walk and stationary for the action of the group $\mathbb{G}=\mathbb{R}^d$ or $\mathbb{G}=\mathbb{Z}^d$. By combining two-scale convergence and Palm theory for $\mathbb{G}$-stationary random measures and by developing a cut-off procedure, under suitable second moment conditions we prove for almost all environments the homogenization for the massive Poisson equation of the associated Markov generators. In addition, we obtain the quenched convergence of the $L^2$-Markov semigroup and resolvent of the diffusively rescaled random walk to the corresponding ones of the Brownian motion with covariance matrix $2D$. For symmetric jump rates, the above convergence plays a crucial role in the derivation of hydrodynamic limits when considering multiple random walks with site-exclusion or zero range interaction. We do not require any ellipticity assumption, neither non-degeneracy of the homogenized matrix $D$. Our results cover a large family of models, including e.g. random conductance models on $\mathbb{Z}^d$ and on general lattices (possibly with long conductances), Mott variable range hopping, simple random walks on Delaunay triangulations, simple random walks on supercritical percolation clusters.

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