Fei Pu

activity

20182023

most citedSpatial ergodicity for SPDEs via a Poincaré-type inequality

13 citations · 22 across the 5 of their papers we have counts for

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Showing math.PRShow all

9 papers · 1 filter

math.PR2021★ 2 cited

Gaussian fluctuation for Gaussian Wishart matrices of overall correlation

Ivan Nourdin, Fei Pu

In this note, we study the Gaussian fluctuations for the Wishart matrices $d^{- 1} X_{n, d} X_{n, d}^{T}$ , where $X_{n, d}$ is a $n \times d$ random matri…

math.PR2020★ 7 cited

Central limit theorems for spatial averages of the stochastic heat equation via Malliavin-Stein's method

Le Chen, Davar Khoshnevisan, David Nualart +1

Suppose that ${u (t, x)}_{t > 0, x \in R^{d}}$ is the solution to a $d$ -dimensional stochastic heat equation driven by a Gaussian noise that is white in time and has a spat…

math.PR2020

Gaussian fluctuation for spatial average of parabolic Anderson model with Neumann/Dirichlet/periodic boundary conditions

Fei Pu

Consider the parabolic Anderson model $\partial_{t} u = \frac{1}{2} \partial_{x}^{2} u + u η$ on the interval $[0, L]$ with Neumann, Dirichlet or periodic boundary conditions, driven by space…

math.PR2020

Spatial stationarity, ergodicity and CLT for parabolic Anderson model with delta initial condition in dimension $d \geq 1$

Davar Khoshnevisan, David Nualart, Fei Pu

Suppose that ${u (t, x)}_{t > 0, x \in R^{d}}$ is the solution to a $d$ -dimensional parabolic Anderson model with delta initial condition and driven by a Gaussian noise tha…

math.PR2019

Spatial ergodicity for SPDEs via Poincaré-type inequalities

Le Chen, Davar Khoshnevisan, David Nualart +1

Consider a parabolic stochastic PDE of the form $\partial_{t} u = \frac{1}{2} Δ u + σ (u) η$ , where $u = u (t, x)$ for $t \geq 0$ and $x \in R^{d}$ , $σ : R \to R$ is…

math.PR2019★ 13 cited

Spatial ergodicity for SPDEs via a Poincaré-type inequality

Le Chen, Davar Khoshnevisan, Fei Pu

Consider a parabolic stochastic PDE of the form $\partial_{t} u = \frac{1}{2} Δ u + σ (u) η$ , where $u = u (t, x)$ for $t \geq 0$ and $x \in R^{d}$ , $σ : R \to R$ is Lipschit…