21 citations · 78 across the 21 of their papers we have counts for
6 papers · 1 filter
Quantitative asymptotic error analysis and Richardson extrapolation for exponential Euler approximations of stochastic wave equations
Jianbo Cui, Diancong Jin, Huagui Liu +1
We establish quantitative weak convergence for the joint terminal law of the solution to a stochastic wave equation and the normalized error of its exponential Euler approximation.…
Fundamental weak convergence theorem for stochastic Volterra integral equations and its applications
Xinjie Dai, Qijiao Yin, Diancong Jin
We study weak convergence rates of numerical approximations for stochastic Volterra integral equations (SVIEs), a class of non-Markovian models that arises naturally in stochastic…
A uniform-in-time weakly convergent explicit numerical method for the underdamped Langevin equation with polynomial potentials
Diancong Jin
The underdamped Langevin equation is a fundamental model in statistical mechanics for sampling Gibbs measures and simulating molecular dynamics, for which numerical methods with un…
Splitting AVF method for generalized Langevin equations: probability density function and geometric ergodicity
Xinjie Dai, Xingyu Liu, Diancong Jin +1
The generalized Langevin equation (GLE) constitutes a fundamental model for describing nonequilibrium dynamics with memory effects. To overcome the numerical challenges arising fro…
Asymptotic error distribution of Mittag--Leffler Euler method for a fractional stochastic differential equation
Xinjie Dai, Baiping Zhang, Diancong Jin
In this paper, we investigate the asymptotic distribution of the normalized error for the Mittag--Leffler Euler (MLE) method applied to a class of multidimensional fractional stoch…
Asymptotic error distribution for tamed Euler method with coupled monotonicity condition
Xinjie Dai, Diancong Jin, Jiaoyang Xu
This paper establishes the asymptotic error distribution of the tamed Euler method for stochastic differential equations (SDEs) with a coupled monotonicity condition, that is, the…