Year: 2019
Type: article
Abstract: Multirate time integration methods apply different step sizes to resolve different components of a system based on local activity levels. This local selection of step sizes allows increased computational efficiency while achieving the desired solution accuracy. While the multirate idea is elegant and has been around for decades, multirate methods are not yet widely used in applications. This is due, in part, to the difficulties created by the construction of high-order multirate schemes. Seeking to overcome these challenges, this work focuses on the design of practical high-order multirate methods using the theoretical framework of multirate general-structure additive Runge--Kutta (MrGARK) methods [M. Günther and A. Sandu, Numer. Math., 133 (2016), pp. 497--524], which provides generic order conditions and linear and nonlinear stability analyses. A set of design criteria for practical multirate methods is defined herein: method coefficients should be generic in the step size ratio but should not depend strongly on this ratio, unnecessary coupling between the fast and the slow components should be avoided, and the step size controllers should adjust both the micro- and the macrosteps. Using these criteria, we develop MrGARK schemes of up to order four that are explicit-explicit (both the fast and slow component are treated explicitly), implicit-explicit (implicit in the fast component and explicit in the slow one), and explicit-implicit (explicit in the fast component and implicit in the slow one). Numerical experiments verify the accuracy of these new schemes.
DOI: 10.1137/18m1182875
Authors: Arash Sarshar, Steven Roberts, Adrian Sandu
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G1523888516, Optimal Design of Constrained Multibody Dynamical Systems, CIF:Small: General Linear Time-stepping Methods for Large-Scale Simulations, G4503139999, G5350959906, G559745938, G6071252945, G6354276058, A Fully Discrete Framework for the Adaptive Solution of Inverse Problems, G7830320759, Collaborative Research: A Computational Framework for Assessing the Observation Impact in Air Quality Forecasting, G8072043338, G8390931773
OpenAlex ID: W2798335277