activity
20162026
most citedStrong Convergence Rate of Splitting Schemes for Stochastic Nonlinear Schrödinger Equations

63 citations · 174 across the 31 of their papers we have counts for

collaborators

32 papers

math.NA2026

Strong error analysis of a temporal approximation for stochastic Korteweg-de Vries equation with small additive noise

Jianbo Cui, Raffaele D'Ambrosio, Stefano Di Giovacchino +1

We study strong temporal approximation of periodic stochastic Korteweg--de Vries equation driven by small additive \(Q\)-Wiener noise of amplitude \(\mathcal O(\varepsilon)\), \(0<…

math.NA2026

A regularized truncated finite element method for degenerate parabolic stochastic PDE on non-compact graph

Jianbo Cui, Mihály Kovács, Derui Sheng

We study the numerical approximation of a class of degenerate parabolic stochastic partial differential equations on non-compact metric graphs, which naturally arise in the asympto…

math.NA2023

Regularization effect of noise on fully discrete approximation for stochastic reaction-diffusion equation near sharp interface limit

Jianbo Cui

To capture and simulate geometric surface evolutions, one effective approach is based on the phase field methods. Among them, it is important to design and analyze numerical approx…

math.NA2023★ 1 cited

Polynomial weak approximation for stochastic reaction-diffusion equations near the sharp interface limit

Jianbo Cui, Liying Sun

We study weak approximation for stochastic reaction-diffusion equations in the sharp-interface regime, where the diffuse interface thickness \(ε\) is small and the dependence of nu…

math.AP2023

Quantifying the effect of random dispersion for logarithmic Schrödinger equation

Jianbo Cui, Liying Sun

This paper is concerned with the random effect of the noise dispersion for stochastic logarithmic Schrödinger equation emerged from the optical fibre with dispersion management. Th…

math.NA2023★ 1 cited

Energy regularized models for logarithmic SPDEs and their numerical approximations

Jianbo Cui, Dianming Hou, Zhonghua Qiao

Understanding the properties of the stochastic phase field models is crucial to model processes in several practical applications, such as soft matters and phase separation in rand…