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Scale-resolving simulations of high Reynolds number incompressible flows are often limited by the Courant-Friedrichs-Lewy (CFL) stability restriction imposed by explicit time-stepping schemes, resulting in small time step sizes and long…

Fluid Dynamics · Physics 2026-04-20 Henrik Wüstenberg , Alexandra Liosi , Spencer J. Sherwin , Joaquim Peiró , David Moxey

Implicit-explicit (IMEX) time stepping methods can efficiently solve differential equa- tions with both stiff and nonstiff components. IMEX Runge-Kutta methods and IMEX linear multistep methods have been studied in the literature. In this…

Numerical Analysis · Mathematics 2013-03-26 Hong Zhang , Adrian Sandu

On the one hand, the explicit Euler scheme fails to converge strongly to the exact solution of a stochastic differential equation (SDE) with a superlinearly growing and globally one-sided Lipschitz continuous drift coefficient. On the other…

Numerical Analysis · Mathematics 2012-09-13 Martin Hutzenthaler , Arnulf Jentzen , Peter E. Kloeden

This paper develops methods for numerically solving stochastic delay-differential equations (SDDEs) with multiple fixed delays that do not align with a uniform time mesh. We focus on numerical schemes of strong convergence orders $1/2$ and…

Numerical Analysis · Mathematics 2026-05-05 Mitchell T. Griggs , Kevin Burrage , Pamela M. Burrage

We carry out a stability and convergence analysis of a fully discrete scheme for the time-dependent Navier-Stokes equations resulting from combining an $H(\mathrm{div}, \Omega)$-conforming discontinuous Galerkin spatial discretization, and…

Numerical Analysis · Mathematics 2025-10-22 L. Beirão da Veiga , F. Dassi , S. Gómez

For many systems of differential equations modeling problems in science and engineering, there are often natural splittings of the right hand side into two parts, one of which is non-stiff or mildly stiff, and the other part is stiff. Such…

Numerical Analysis · Mathematics 2018-11-07 Giuseppe Izzo , Zdzislaw Jackiewicz

In the numerical solution of partial differential equations using a method-of-lines approach, the availability of high order spatial discretization schemes motivates the development of sophisticated high order time integration methods. For…

Numerical Analysis · Computer Science 2016-11-25 Hong Zhang , Adrian Sandu , Sebastien Blaise

For stochastic differential equations (SDEs) with a superlinearly growing and globally one-sided Lipschitz continuous drift coefficient, the classical explicit Euler scheme fails to converge strongly to the exact solution. Recently, an…

Numerical Analysis · Mathematics 2014-08-26 Xiaojie Wang , Siqing Gan

We provide a primer to numerical methods based on Taylor series expansions such as generalized finite difference methods and collocation methods. We provide a detailed benchmarking strategy for these methods as well as all data files…

A method of representation of a solution as segments of the series in powers of the step of the independent variable is expanded for solving complex systems of ordinary differential equations (ODE): the Lorenz system and other systems. A…

Numerical Analysis · Computer Science 2014-05-26 Vladimir Aristov , Andrey Stroganov

We propose some finite element schemes to solve a class of fourth-order nonlinear PDEs, which include the vector-valued Landau--Lifshitz--Baryakhtar equation, the Swift--Hohenberg equation, and various Cahn--Hilliard-type equations with…

Numerical Analysis · Mathematics 2024-11-19 Agus L. Soenjaya , Thanh Tran

A novel efficient and high accuracy numerical method for the time-fractional differential equations (TFDEs) is proposed in this work. We show the equivalence between TFDEs and the integer-order extended parametric differential equations…

Numerical Analysis · Mathematics 2025-05-13 Peng Ding , Zhiping Mao

Schemes with the second-order approximation in time are considered for numerical solving the Cauchy problem for an evolutionary equation of first order with a self-adjoint operator. The implicit two-level scheme based on the Pad\'{e}…

Numerical Analysis · Computer Science 2015-04-17 P. N. Vabishchevich

We provide of a method to integrate first order non-linear systems of differential equations with variable coefficients. It determines approximate solutions given initial or boundary conditions or even for Sturm-Liouville problems. This…

Classical Analysis and ODEs · Mathematics 2025-03-05 Manuel Gadella , Luis P. Lara

To solve the Cahn-Hilliard equation numerically, a new time integration algorithm is proposed, which is based on a combination of the Eyre splitting and the local iteration modified (LIM) scheme. The latter is employed to tackle the…

Numerical Analysis · Mathematics 2024-09-27 M. A. Botchev , I. A. Fahurdinov , E. B. Savenkov

Here we present a new approach to deal with first order ordinary differential equations (1ODEs), presenting functions. This method is an alternative to the one we have presented in [1]. In [2], we have establish the theoretical background…

Classical Analysis and ODEs · Mathematics 2023-01-06 L. G. S. Duarte , L. A. C. P. da Mota , A. B. M. M. Queiroz

We present an original study on the numerical stabiliy of explicit schemes solving the incompressible Euler equations on an open domain with slipping boundary conditions. Relying on the skewness property of the non-linear term, we…

Numerical Analysis · Mathematics 2007-12-17 Erwan Deriaz

This paper is devoted to order-one explicit approximations of random periodic solutions to multiplicative noise driven stochastic differential equations (SDEs) with non-globally Lipschitz coefficients. The existence of the random periodic…

Probability · Mathematics 2025-01-06 Yujia Guo , Xiaojie Wang , Yue Wu

In this paper, we propose a class of super-schemes for efficiently solving nonlinear unconstrained optimization problems. The proposed approach introduces two novel choices of step-size parameters, leading to efficient descent directions…

Optimization and Control · Mathematics 2026-04-24 Tugal Zhanlav , Lkhamsuren Altangerel , Khuder Otgondorj

We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network. Stable neural flows provide an implicit guarantee on…

Machine Learning · Computer Science 2020-03-19 Stefano Massaroli , Michael Poli , Michelangelo Bin , Jinkyoo Park , Atsushi Yamashita , Hajime Asama