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This paper extends deterministic notions of Strong Stability Preservation (SSP) to the stochastic setting, enabling nonlinearly stable numerical solutions to stochastic differential equations (SDEs) and stochastic partial differential…

Numerical Analysis · Mathematics 2024-12-10 James Woodfield

We present a new method for obtaining sharp asymptotics of solutions of the defocussing nonlinear Schr\"odinger (NLS) equation, based on dbar methods and under essentially minimal regularity assumptions on initial data.

Analysis of PDEs · Mathematics 2008-05-20 M. Dieng , K. D. T. -R. McLaughlin

This manuscript is dedicated to the numerical approximation of super-linear slow-fast stochastic differential equations (SFSDEs). Borrowing the heterogeneous multiscale idea, we propose an explicit multiscale Euler-Maruyama scheme suitable…

Numerical Analysis · Mathematics 2025-03-18 Yuanping Cui , Xiaoyue Li , Xuerong Mao

We propose a novel numerical approach for nonlocal diffusion equations [8] with integrable kernels, based on the relationship between the backward Kolmogorov equation and backward stochastic differential equations (BSDEs) driven by L\`{e}vy…

Numerical Analysis · Mathematics 2015-07-28 Guannan Zhang , Weidong Zhao , Clayton Webster , Max Gunzburger

This paper addresses the numerical solution of the two-dimensional Navier--Stokes (NS) equations with nonsmooth initial data in the $L^2$ space, which is the critical space for the two-dimensional NS equations to be well-posed. In this…

Numerical Analysis · Mathematics 2025-10-02 Buyang Li , Qiqi Rao , Hui Zhang , Zhi Zhou

We study the asymptotic stability of the semi-discrete (SD) numerical method for the approximation of stochastic differential equations. Recently, we examined the order of $\mathcal L^2$-convergence of the truncated SD method and showed…

Numerical Analysis · Mathematics 2020-08-10 Nikolaos Halidias , Ioannis S. Stamatiou

A semi-Lagrangian discontinuous finite element scheme based on the characteristic Galerkin method (CSLDG) is investigated, which directly discretizes an integral invariant model derived from the coupling of the transport equation and its…

Numerical Analysis · Mathematics 2026-05-08 Zhengrong Xie

We study a deformation of the nonlinear Schr\"odinger equation recently derived in the context of deformation of hierarchies of integrable systems. This systematic method also led to known integrable equations such as the Camassa-Holm…

Exactly Solvable and Integrable Systems · Physics 2015-08-24 Alexis Arnaudon

We consider the implementation of the split-step method where the linear part of the nonlinear Schr\"odinger equation is solved using a finite-difference discretization of the spatial derivative. The von Neumann analysis predicts that this…

Numerical Analysis · Mathematics 2012-08-03 T. I. Lakoba

While symplectic integration methods based on operator splitting are well established in many branches of science, high order methods for Hamiltonian systems that split in more than two parts have not been studied in great detail. Here, we…

Computational Physics · Physics 2015-06-15 Ch. Skokos , E. Gerlach , J. D. Bodyfelt , G. Papamikos , S. Eggl

Stochastic dynamical systems with continuous symmetries arise commonly in nature and often give rise to coherent spatio-temporal patterns. However, because of their random locations, these patterns are not well captured by current order…

Computational Physics · Physics 2021-10-25 Saviz Mowlavi , Themistoklis P. Sapsis

This paper describes an updated exponential Fourier based split-step method that can be applied to a greater class of partial differential equations than previous methods would allow. These equations arise in physics and engineering, a…

Numerical Analysis · Mathematics 2016-06-14 Haider Zia

We discuss mathematical methods to derive Nonlinear Schr\"odinger equations (NLS) in "low dimensional" settings, i.e. the 3-dimensional physical space e.g. to 2 or 1 space dimensions. Beside from the case the system exhibits an internal…

Computational Physics · Physics 2023-12-19 Peter Allmer

We introduce a simple and stable computational method for ill-posed partial differential equation (PDE) problems. The method is based on Schr\"odingerization, introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y.…

Numerical Analysis · Mathematics 2024-11-11 Shi Jin , Nana Liu , Chuwen Ma

We consider the linear and non linear cubic Schr\"odinger equations with periodic boundary conditions, and their approximations by splitting methods. We prove that for a dense set of arbitrary small time steps, there exists numerical…

Numerical Analysis · Mathematics 2013-11-20 Erwan Faou , Tiphaine Jézéquel

The paper establishes the strong convergence rates of a spatio-temporal full discretization of the stochastic wave equation with nonlinear damping in dimension one and two. We discretize the SPDE by applying a spectral Galerkin method in…

Numerical Analysis · Mathematics 2024-12-30 Meng Cai , David Cohen , Xiaojie Wang

In this paper, we propose and analyze an explicit time-stepping scheme for a spatial discretization of stochastic Cahn--Hilliard equation with additive noise. The fully discrete approximation combines a spectral Galerkin method in space…

Numerical Analysis · Mathematics 2023-08-31 Meng Cai , Ruisheng Qi , Xiaojie Wang

We present different techniques to numerically solve the equations of motion for the widely studied Discrete Nonlinear Schroedinger equation (DNLS). Being a Hamiltonian system, the DNLS requires symplectic routines for an efficient…

Computational Physics · Physics 2013-04-08 Mario Mulansky

A large toolbox of numerical schemes for dispersive equations has been established, based on different discretization techniques such as discretizing the variation-of-constants formula (e.g., exponential integrators) or splitting the full…

Numerical Analysis · Mathematics 2024-05-20 Frédéric Rousset , Katharina Schratz

Recent work has introduced a simple numerical method for solving partial differential equations (PDEs) with deep neural networks (DNNs). This paper reviews and extends the method while applying it to analyze one of the most fundamental…

Machine Learning · Computer Science 2019-05-14 Craig Michoski , Milos Milosavljevic , Todd Oliver , David Hatch
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