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The paper aims at developing low-storage implicit Runge-Kutta methods which are easy to implement and achieve higher-order of convergence for both the velocity and pressure in the finite volume formulation of the incompressible…

Numerical Analysis · Mathematics 2019-07-08 Jiawei Wan , Ahsan Kareem , Haili Liao , Yunzhu Cai

Semi-implicit spectral deferred correction (SDC) methods provide a systematic approach to construct time integration methods of arbitrarily high order for nonlinear evolution equations including conservation laws. They converge towards $A$-…

Numerical Analysis · Mathematics 2025-01-29 Joerg Stiller

Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine learning. We present…

Machine Learning · Computer Science 2024-12-17 Hong Zhang , Ying Liu , Romit Maulik

In this paper a new Runge-Kutta type scheme is introduced for nonlinear stochastic partial differential equations (SPDEs) with multiplicative trace class noise. The proposed scheme converges with respect to the computational effort with a…

Numerical Analysis · Mathematics 2012-04-03 Xiaojie Wang , Siqing Gan

We study solutions to nonlinear hyperbolic systems with fully nonlinear relaxation terms in the limit of, both, infinitely stiff relaxation and arbitrary late time. In this limit, the dynamics is governed by effective systems of parabolic…

Analysis of PDEs · Mathematics 2012-10-18 Sebastiano Boscarino , Philippe G. LeFloch , Giovanni Russo

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

Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic Gamma distributed DDEs are not currently available. Accordingly, modellers often resort…

Numerical Analysis · Mathematics 2021-04-09 Tyler Cassidy , Peter Gillich , Antony R. Humphries , Christiaan H. van Dorp

We study gradient-based optimization methods obtained by direct Runge-Kutta discretization of the ordinary differential equation (ODE) describing the movement of a heavy-ball under constant friction coefficient. When the function is high…

Optimization and Control · Mathematics 2019-05-30 Jingzhao Zhang , Suvrit Sra , Ali Jadbabaie

This work considers multirate generalized-structure additively partitioned Runge-Kutta (MrGARK) methods for solving stiff systems of ordinary differential equations (ODEs) with multiple time scales. These methods treat different partitions…

Numerical Analysis · Mathematics 2022-01-19 Steven Roberts , John Loffeld , Arash Sarshar , Carol S. Woodward , Adrian Sandu

The application of Runge-Kutta schemes designed to enjoy a large region of absolute stability can significantly increase the efficiency of numerical methods for PDEs based on a method of lines approach. In this work we investigate the…

Numerical Analysis · Mathematics 2007-05-23 Fausto Cavalli , Giovanni Naldi , Gabriella Puppo , Matteo Semplice

A coercivity property of temporal convolution operators is an essential tool in the analysis of time-dependent boundary integral equations and their space and time discretisations. It is known that this coercivity property is inherited by…

Numerical Analysis · Mathematics 2017-02-28 Lehel Banjai , Christian Lubich

We analyze the stability and accuracy (up to third order) of a new family of implicit-explicit Runge-Kutta (IMEX RK) methods. This analysis expedites development of methods with various balances in the number of explicit stages and implicit…

Numerical Analysis · Mathematics 2019-06-19 Andrew Steyer , Christopher J. Vogl , Mark Taylor , Oksana Guba

In this work, we investigate the numerical approximation of the second order non-autonomous semilnear parabolic partial differential equation (PDE) using the finite element method. To the best of our knowledge, only the linear case is…

Numerical Analysis · Mathematics 2020-01-27 Antoine Tambue , Jean Daniel Mukam

Finite element discretization of time dependent problems also require effective time-stepping schemes. While implicit Runge-Kutta methods provide favorable accuracy and stability problems, they give rise to large and complicated systems of…

Numerical Analysis · Mathematics 2023-05-01 Robert C. Kirby

Exponential integrators are explicit methods for solving ordinary differential equations that treat linear behaviour exactly. The stiff-order conditions for exponential integrators derived in a Banach space framework by Hochbruck and…

Computational Physics · Physics 2023-03-28 Thoma Zoto , John C. Bowman

Modern deep learning algorithms use variations of gradient descent as their main learning methods. Gradient descent can be understood as the simplest Ordinary Differential Equation (ODE) solver; namely, the Euler method applied to the…

Machine Learning · Computer Science 2025-05-20 Benoit Dherin , Michael Munn , Hanna Mazzawi , Michael Wunder , Sourabh Medapati , Javier Gonzalvo

Systems driven by multiple physical processes are central to many areas of science and engineering. Time discretization of multiphysics systems is challenging, since different processes have different levels of stiffness and characteristic…

Numerical Analysis · Mathematics 2022-01-19 Adrian Sandu , Michael Günther , Steven Roberts

We consider semilinear evolution equations for which the linear part is normal and generates a strongly continuous semigroup and the nonlinear part is sufficiently smooth on a scale of Hilbert spaces. We approximate their semiflow by an…

Numerical Analysis · Mathematics 2015-10-22 Marcel Oliver , Claudia Wulff

This work generalizes the additively partitioned Runge-Kutta methods by allowing for different stage values as arguments of different components of the right hand side. An order conditions theory is developed for the new family of…

Numerical Analysis · Computer Science 2013-10-22 Adrian Sandu , Michael Guenther

Strong stability preserving (SSP) integrators for initial value ODEs preserve temporal monotonicity solution properties in arbitrary norms. All existing SSP methods, including implicit methods, either require small step sizes or achieve…

Numerical Analysis · Mathematics 2012-03-27 David I. Ketcheson