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Stochastic differential equations (SDE) often exhibit large random transitions. This property, which we denote as pathwise stiffness, causes transient bursts of stiffness which limit the allowed step size for common fixed time step explicit…

Numerical Analysis · Mathematics 2018-04-13 Christopher Rackauckas , Qing Nie

Stabilized explicit methods are particularly efficient for large systems of stiff stochastic differential equations (SDEs) due to their extended stability domain. However, they loose their efficiency when a severe stiffness is induced by…

Numerical Analysis · Mathematics 2021-08-13 Assyr Abdulle , Giacomo Rosilho de Souza

We introduce a new approach for designing numerical schemes for stochastic differential equations (SDEs). The approach, which we have called direction and norm decomposition method, proposes to approximate the required solution $X_t$ by…

Numerical Analysis · Mathematics 2017-02-21 C. M. Mora , H. A. Mardones , J. C. Jimenez , M. Selva , R. Biscay

A novel second order family of explicit stabilized Runge-Kutta-Chebyshev methods for advection-diffusion-reaction equations is introduced. The new methods outperform existing schemes for relatively high Peclet number due to their favorable…

Numerical Analysis · Mathematics 2023-06-09 Ibrahim Almuslimani

Explicit stabilized methods are an efficient alternative to implicit schemes for the time integration of stiff systems of differential equations in large dimension. In this paper, we derive explicit stabilized integrators of orders one and…

Numerical Analysis · Mathematics 2023-06-09 Ibrahim Almuslimani , Gilles Vilmart

A new explicit stochastic scheme of order 1 is proposed for solving commutative stochastic differential equations (SDEs) with non-globally Lipschitz continuous coefficients. The proposed method is a semi-tamed version of Milstein scheme to…

Numerical Analysis · Mathematics 2021-10-13 Yulong Liu , Yuanling Niu , Xiujun Cheng

A new Chebyshev-type family of stabilized explicit methods for solving mildly stiff ODEs is presented. Besides conventional conditions of order and stability we impose an additional restriction on the methods: their stability function must…

Numerical Analysis · Mathematics 2025-04-02 Boris Faleichik , Andrew Moisa

In this study, we propose high-order implicit and semi-implicit schemes for solving ordinary differential equations (ODEs) based on Taylor series expansion. These methods are designed to handle stiff and non-stiff components within a…

Numerical Analysis · Mathematics 2024-09-19 S. Boscarino , E. Macca

Many time-dependent partial differential equations (PDEs) can be transformed into an ordinary differential equations (ODEs) containing moderately stiff and non-stiff terms after spatial semi-discretization. In the present paper, we…

Numerical Analysis · Mathematics 2025-09-23 Xiao Tang , Junwei Huang

Explicit stabilized integrators are an efficient alternative to implicit or semi-implicit methods to avoid the severe timestep restriction faced by standard explicit integrators applied to stiff diffusion problems. In this paper, we provide…

Numerical Analysis · Mathematics 2022-12-14 Assyr Abdulle , Charles-Edouard Bréhier , Gilles Vilmart

We use the linear scalar SDE as a test problem to show that it is possible to construct almost sure stable first-order weak balanced schemes based on the addition of stabilizing functions to the drift terms. Then, we design balanced schemes…

Probability · Mathematics 2014-08-26 H. A. Mardones , C. M. Mora

A new, improved split-step backward Euler (SSBE) method is introduced and analyzed for stochastic differential delay equations(SDDEs) with generic variable delay. The method is proved to be convergent in mean-square sense under conditions…

Numerical Analysis · Mathematics 2011-07-05 Xiaojie Wang , Siqing Gan

This paper examines convergence and stability of the two classes of theta-Milstein schemes for stochastic differential equations (SDEs) with non-global Lipschitz continuous coefficients: the split-step theta-Milstein (SSTM) scheme and the…

Numerical Analysis · Mathematics 2015-01-16 Xiaofeng Zong , Fuke Wu , Guiping Xu

This paper introduces the Runge-Kutta Chebyshev descent method (RKCD) for strongly convex optimisation problems. This new algorithm is based on explicit stabilised integrators for stiff differential equations, a powerful class of numerical…

Optimization and Control · Mathematics 2020-06-30 Armin Eftekhari , Bart Vandereycken , Gilles Vilmart , Konstantinos C. Zygalakis

The concept of effective order is a popular methodology in the deterministic literature for the construction of efficient and accurate integrators for differential equations over long times. The idea is to enhance the accuracy of a…

Numerical Analysis · Mathematics 2016-08-18 Gilles Vilmart

Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an…

Machine Learning · Computer Science 2025-01-28 YongKyung Oh , Dong-Young Lim , Sungil Kim

We develop a stabilized cut finite element method for the stationary convection diffusion problem on a surface embedded in ${\mathbb{R}}^d$. The cut finite element method is based on using an embedding of the surface into a three…

Numerical Analysis · Mathematics 2018-07-24 Erik Burman , Peter Hansbo , Mats G. Larson , Andre Massing , Sara Zahedi

Propagation characteristics of a wave are defined by the dispersion relationship, from which the governing partial differential equation (PDE) can be recovered. PDEs are commonly solved numerically using the finite-difference (FD) method,…

Numerical Analysis · Mathematics 2021-07-29 Edward Caunt

In this paper we study the stability of explicit finite difference discretizations of linear advection-diffusion equations (ADE) with arbitrary order of accuracy in the context of method of lines. The analysis first focuses on the stability…

Numerical Analysis · Mathematics 2020-06-17 Xianyi Zeng , Md Mahmudul Hasan

In this paper, we first establish well-posedness of McKean-Vlasov stochastic differential equations (McKean-Vlasov SDEs) with common noise, possibly with coefficients having super-linear growth in the state variable. Second, we present…

Probability · Mathematics 2020-06-02 Chaman Kumar , Neelima , Christoph Reisinger , Wolfgang Stockinger
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