ARKODE:用于一步法的灵活IVP求解器基础结构

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Daniel R. Reynolds, David J. Gardner, Carol S. Woodward, Rujeko Chinomona
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引用次数: 0

摘要

本文描述了求解常微分方程(ODE)初值问题的一步时间积分方法的ARKODE库。除了提供标准的显式和对角隐式龙格-库塔方法外,ARKODE还支持一步法,用于处理IVP的加性分裂,包括隐式-显式(ImEx)加性龙格-库塔方法和多率无穷小(MRI)方法。我们介绍了ARKODE在SUNDIALS时间集成和非线性求解器库套件中的作用,这是大型单步方法类常用的核心ARKODE基础设施,以及它使用的“时间步进器”模块,可以轻松地将新算法合并到库中。数值结果显示了日益复杂的示例问题,突出了通过该基础设施提供的算法灵活性,并包括利用ARKODE和SUNDIALS的多种算法特性的更大的多物理场应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ARKODE: A Flexible IVP Solver Infrastructure for One-step Methods

We describe the ARKODE library of one-step time integration methods for ordinary differential equation (ODE) initial-value problems (IVPs). In addition to providing standard explicit and diagonally implicit Runge–Kutta methods, ARKODE supports one-step methods designed to treat additive splittings of the IVP, including implicit-explicit (ImEx) additive Runge–Kutta methods and multirate infinitesimal (MRI) methods. We present the role of ARKODE within the SUNDIALS suite of time integration and nonlinear solver libraries, the core ARKODE infrastructure for utilities common to large classes of one-step methods, as well as its use of “time stepper” modules enabling easy incorporation of novel algorithms into the library. Numerical results show example problems of increasing complexity, highlighting the algorithmic flexibility afforded through this infrastructure, and include a larger multiphysics application leveraging multiple algorithmic features from ARKODE and SUNDIALS.

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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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