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Haiyi Wang

Publications and source records attributed to Haiyi Wang.

3 recordsLinked to original sources

Regularization by regular noise: a numerical result

We study a singular stochastic equation driven by a regular noise of fractional Brownian type with Hurst index $H \in (1,\infty)\setminus\mathbb{Z}$ and drift coefficient $b \in \mathcal{C}^\alpha$, where $\alpha > 1 - \frac{1}{2H}$. The strong well-posedness of this equation was first established in [Ger23], a phenomenon referred to as regularization by regular noise. In this note, we provide a corresponding numerical analysis. Specifically, we show that the Euler-Maruyama approximation $X^n$ converges strongly to the unique solution $X$ with rate $n^{-1}$. Furthermore, under the additional assumption $b \in \mathcal{C}^1$, we show that $n(X - X^n)$ converges to a non-trivial limit as $n \to \infty$, thereby confirming that the rate $n^{-1}$ is in fact optimal upper bound for this scheme.

math.PR

A functional Breuer-Major theorem with Poisson noise

We extend the functional Breuer-Major theorem for Gaussians to the Poisson case, where the stationary sequence arises from a Poisson point process. We use the $L^p$ spectral gap inequality of Poisson point process as a tool to prove tightness.

math.PR

Hairer-Quastel universality for KPZ -- polynomial smoothing mechanisms, general nonlinearities and Poisson noise

We consider a class of weakly asymmetric continuous microscopic growth models with polynomial smoothing mechanisms, general nonlinearities and a Poisson type noise. We show that they converge to the KPZ equation after proper rescaling and re-centering, where the coupling constant depends nontrivially on all details of the smoothing and growth mechanisms in the microscopic model. This confirms some of the predictions in [HQ18], and provides a first example of Hairer-Quastel type with both a generic nonlinearity (non-polynomial) and a non-Gaussian noise. The proof builds on the general discretisation framework of regularity structures ([EH19]), and employs the idea of using the spectral gap inequality to control stochastic objects as developed and systematised in [LOTT21,HS24], together with a new observation on the specific structure of the (discrete) Malliavin derivatives in our situation. This structure enables us to reduce the control of mixed $L^p$ spacetime norms (of arbitrarily large $p$) by certain $L^2$-norms in spacetime.

math.PR