21 citations · 78 across the 21 of their papers we have counts for
22 papers
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…
Asymptotic error distribution of numerical methods for parabolic SPDEs with multiplicative noise
Jialin Hong, Diancong Jin, Xu Wang
This paper aims to investigate the asymptotic error distribution of several numerical methods for stochastic partial differential equations (SPDEs) with multiplicative noise. First…
Asymptotic error distributions for diffusion-implicit schemes via appurtenant methods
Diancong Jin
We develop an appurtenant-method framework for analyzing asymptotic error distributions and implement it in two diffusion-implicit settings for stochastic differential equations. T…
Asymptotic error distribution of accelerated exponential Euler method for parabolic SPDEs
Jialin Hong, Diancong Jin, Xu Wang +1
The asymptotic error distribution of numerical methods applied to stochastic ordinary differential equations has been well studied, which characterizes the evolution pattern of the…
Density functions for the overdamped generalized Langevin equation and its Euler--Maruyama method: smoothness and convergence
Xinjie Dai, Diancong Jin
This paper focuses on studying the convergence rate of the density function of the Euler--Maruyama (EM) method, when applied to the overdamped generalized Langevin equation with fr…
Central limit theorem for temporal average of backward Euler--Maruyama method
Diancong Jin
This work focuses on the temporal average of the backward Euler--Maruyama (BEM) method, which is used to approximate the ergodic limit of stochastic ordinary differential equations…
Asymptotics of large deviations of finite difference method for stochastic Cahn--Hilliard equation
Diancong Jin, Derui Sheng
In this work, we establish the Freidlin--Wentzell large deviations principle (LDP) of the stochastic Cahn--Hilliard equation with small noise, which implies the one-point LDP. Furt…
Scheme-Induced Minimum Action Methods for SPDEs: A Variational Framework for Convergence Analysis
Jialin Hong, Diancong Jin, Derui Sheng
The minimum action method (MAM) is an important numerical tool for computing the most probable transition paths and the leading exponential rates of rare-event probabilities for st…
Density convergence of a fully discrete finite difference method for stochastic Cahn--Hilliard equation
Jialin Hong, Diancong Jin, Derui Sheng
This paper focuses on investigating the density convergence of a fully discrete finite difference method when applied to numerically solve the stochastic Cahn--Hilliard equation dr…
Convergence analysis of a finite difference method for stochastic Cahn--Hilliard equation
Jialin Hong, Diancong Jin, Derui Sheng
This paper presents the convergence analysis of the spatial finite difference method (FDM) for the stochastic Cahn--Hilliard equation with Lipschitz nonlinearity and multiplicative…
Large deviations principle for stochastic delay differential equations with super-linearly growing coefficients
Diancong Jin, Ziheng Chen, Tau Zhou
We utilize the weak convergence method to establish the Freidlin--Wentzell large deviations principle (LDP) for stochastic delay differential equations (SDDEs) with super-linearly…
Large deviations principles of sample paths and invariant measures of numerical methods for parabolic SPDEs
Chuchu Chen, Ziheng Chen, Jialin Hong +1
For parabolic stochastic partial differential equations (SPDEs), we show that the numerical methods, including the spatial spectral Galerkin method and further the full discretizat…
Convergence analysis for minimum action methods coupled with a finite difference method
Jialin Hong, Diancong Jin, Derui Sheng
The minimum action method (MAM) is an effective approach to numerically solving minimums and minimizers of Freidlin--Wentzell (F-W) action functionals, which is used to study the m…
Numerically asymptotical preservation of the large deviations principles for invariant measures of Langevin equations
Jialin Hong, Diancong Jin, Derui Sheng +1
In this paper, we focus on two kinds of large deviations principles (LDPs) of the invariant measures of Langevin equations and their numerical methods, as the noise intensity $ε\to…
Large deviations principles for symplectic discretizations of stochastic linear Schrödinger Equation
Chuchu Chen, Jialin Hong, Diancong Jin +1
In this paper, we consider the large deviations principles (LDPs) for the stochastic linear Schrödinger equation and its symplectic discretizations. These numerical discretizations…
The probabilistic superiority of stochastic symplectic methods via large deviations principles
Chuchu Chen, Jialin Hong, Diancong Jin +1
It is well known that symplectic methods have been rigorously shown to be superior to non-symplectic ones especially in long-time computation, when applied to deterministic Hamilto…
Modified averaged vector field methods preserving multiple invariants for conservative stochastic differential equations
Chuchu Chen, Jialin Hong, Diancong Jin
A novel class of conservative numerical methods for general conservative Stratonovich stochastic differential equations with multiple invariants is proposed and analyzed. These met…