In this paper we generalize the polynomial time integration framework to additively partitioned initial value problems. The framework we present is general and enables the construction of many new families of additive integrators with arbitrary order-of-accuracy and varying degree of implicitness. In this first work, we focus on a new class of implicit-explicit polynomial block methods that are based on fully-implicit Runge-Kutta methods with Radau nodes. We show that the new integrators have improved stability compared to existing IMEX Runge-Kutta methods, while also being more computationally efficient due to recent developments in preconditioning techniques for solving the associated systems of nonlinear equations. For PDEs on periodic domains where the implicit component is trivial to invert, we will show how parallelization of the right-hand-side evaluations can be exploited to obtain significant speedup compared to existing serial IMEX Runge-Kutta methods. For parallel (in space) finite-element discretizations, the new methods obtain accuracy several orders of magnitude lower than existing IMEX Runge-Kutta methods, and/or obtain a given accuracy as much as 16 times faster in terms of computational runtime.
翻译:在本文中,我们将多元时间整合框架推广为叠加分割初始值问题。我们提出的框架是一般性的,能够建造许多新的添加式集成器新组群,这些添加式集成器具有任意的定序性以及不同程度的隐含性。在本文的首份工作中,我们侧重于基于完全隐含的龙格-库塔方法的新的一类隐含的显性多米方块方法,与Radau节点相比,这些基于完全隐含的龙格-库塔方法。我们表明,新的集成器比现有的IMEX 龙格-库塔方法提高了稳定性,同时由于最近在解决非线性方程式相关系统的先决条件技术方面的发展,也提高了计算效率。对于定期域的PDE,其中的隐含部分微不足道倒置,我们将展示如何利用右侧评价的平行化,以获得与现有的序列IMEX Runge-库塔方法相比的重大加速率。关于平行(空间)的定分解方法,新的方法获得了比现有的IMEX Runge-库塔方法在16个时间进行快速的精确度。