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Low-storage explicit Runge-Kutta schemes are particularly popular for the numerical integration of time-dependent partial differential equations based on the method-of-lines due to their efficiency and their reduced memory requirements. We…

Numerical Analysis · Mathematics 2026-04-07 Sergio Blanes , Alejandro Escorihuela-Tomàs

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

Mixed-precision methods combine low and high precision arithmetics to exploit low precision computational speed and high precision accuracy. Large ODE systems that contain many heterogeneous interactions lead to a high computational cost…

Numerical Analysis · Mathematics 2026-05-25 Mouhamad Al-Sayed , Samuel Bernard , Arsène Marzorati , Jonathan Rouzaud-Cornabas

The manuscript presents a new technique for computing the exponential of skew-Hermitian operators. Principal advantages of the proposed method include: stability even for large time-steps, the possibility to parallelize in time over many…

Numerical Analysis · Mathematics 2014-02-24 T. S. Haut , T. Babb , P. G. Martinsson , B. A. Wingate

Runge-Kutta time-stepping methods in general suffer from order reduction: the observed order of convergence may be less than the formal order when applied to certain stiff problems. Order reduction can be avoided by using methods with high…

Numerical Analysis · Mathematics 2023-08-17 David Ketcheson , Benjamin Seibold , David Shirokoff , Dong Zhou

Many complex applications require the solution of initial-value problems where some components change fast, while others vary slowly. Multirate schemes apply different step sizes to resolve different components of the system, according to…

Numerical Analysis · Mathematics 2021-02-23 Michael Guenther , Adrian Sandu

The Runge--Kutta (RK) discontinuous Galerkin (DG) method is a mainstream numerical algorithm for solving hyperbolic equations. In this paper, we use the linear advection equation in one and two dimensions as a model problem to prove the…

Numerical Analysis · Mathematics 2024-10-02 Zheng Sun

This work presents algorithms for the efficient implementation of discontinuous Galerkin methods with explicit time stepping for acoustic wave propagation on unstructured meshes of quadrilaterals or hexahedra. A crucial step towards…

Numerical Analysis · Computer Science 2019-03-06 Svenja Schoeder , Katharina Kormann , Wolfgang Wall , Martin Kronbichler

In order to solve continuous-time optimal control problems, direct methods transcribe the infinite-dimensional problem to a nonlinear program (NLP) using numerical integration methods. In cases where the integration error can be manipulated…

Optimization and Control · Mathematics 2025-03-18 Jakob Harzer , Jochem De Schutter , Moritz Diehl

We consider quadrature formulas of high order in time based on Radau-type, L-stable implicit Runge-Kutta schemes to solve time dependent stiff PDEs. Instead of solving a large nonlinear system of equations, we develop a method that performs…

Numerical Analysis · Mathematics 2016-04-04 Max Duarte , Matthew Emmett

The residual-based variational multiscale (VMS) formulation has achieved remarkable success in large-eddy simulation of turbulent flows. However, its temporal discretization has largely remained limited to second-order implicit schemes. The…

Fluid Dynamics · Physics 2025-12-09 Yujie Sun , Chi Ding , Ju Liu

Sampling from a high-dimensional probability distribution is a fundamental algorithmic task arising in wide-ranging applications across multiple disciplines, including scientific computing, computational statistics and machine learning.…

Statistics Theory · Mathematics 2026-05-11 Bin Yang , Xiaojie Wang

In this survey, we provide an in-depth investigation of exponential Runge-Kutta methods for the numerical integration of initial-value problems. These methods offer a valuable synthesis between classical Runge-Kutta methods, introduced more…

Numerical Analysis · Mathematics 2026-04-27 Alessia andò , Nicolò Cangiotti , Mattia Sensi

We investigate the RG-time integration of the effective potential in the functional renormalization group in the presence of spontaneous symmetry breaking and its subsequent convexity restoration on the example of a scalar theory in $d=3$.…

High Energy Physics - Theory · Physics 2023-06-21 Friederike Ihssen , Franz R. Sattler , Nicolas Wink

When a high dimension system of ordinary differential equations is solved numerically, the computer memory capacity may be compromised. Thus, for such systems, it is important to incorporate low memory usage to some other properties of the…

Numerical Analysis · Mathematics 2018-09-14 I. Higueras , T. Roldan

Exponential Runge-Kutta methods constitute efficient integrators for semilinear stiff problems. So far, however, explicit exponential Runge-Kutta methods are available in the literature up to order 4 only. The aim of this paper is to…

Classical Analysis and ODEs · Mathematics 2016-06-20 Vu Thai Luan , Alexander Ostermann

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

Numerical Analysis · Mathematics 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

Implicit-explicit (IMEX) Runge-Kutta methods play a major rule in the numerical treatment of differential systems governed by stiff and non-stiff terms. This paper discusses order conditions and symplecticity properties of a class of IMEX…

Numerical Analysis · Mathematics 2012-02-07 Michael Herty , Lorenzo Pareschi , Sonja Steffensen

We construct a family of embedded pairs for optimal strong stability preserving explicit Runge-Kutta methods of order $2 \leq p \leq 4$ to be used to obtain numerical solution of spatially discretized hyperbolic PDEs. In this construction,…

Numerical Analysis · Mathematics 2022-05-17 Sidafa Conde , Imre Fekete , John N. Shadid

Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to new tasks, as well as (ii) having a shared representation…

Machine Learning · Computer Science 2019-10-21 Daniel Jiwoong Im , Yibo Jiang , Nakul Verma