Construction of Additive Semi-Implicit Runge-Kutta methods with low-storage requirements

TL;DR

This paper develops low-storage Additive Semi-Implicit Runge-Kutta methods for efficiently solving large, stiff, additive differential systems, balancing accuracy, stability, and memory constraints.

Contribution

It introduces two second-order, 3-stage ASIRK schemes with low-storage requirements and stiff accuracy, suitable for large stiff systems.

Findings

Numerical experiments demonstrate the advantages of the new methods.

The schemes achieve a balance of accuracy, stability, and low memory usage.

Low-storage schemes are effective for large-scale stiff problems.

Abstract

Space discretization of some time-dependent partial differential equations gives rise to systems of ordinary differential equations in additive form whose terms have different stiffness properties. In these cases, implicit methods should be used to integrate the stiff terms while efficient explicit methods can be used for the non-stiff part of the problem. However, for systems with a large number of equations, memory storage requirement is also an important issue. When the high dimension of the problem compromises the computer memory capacity, it is important to incorporate low memory usage to some other properties of the scheme. In this paper we consider Additive Semi-Implicit Runge-Kutta (ASIRK) methods, a class of implicitexplicit Runge-Kutta methods for additive differential systems. We construct two second order 3-stage ASIRK schemes with low-storage requirements. Having in mind problems with stiffness parameters, besides accuracy and stability properties, we also impose stiff accuracy conditions. The numerical experiments done show the advantages of the new methods.