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, and possess high stage order. We show that the new fully-implicit-explicit (FIMEX) 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 can achieve orders of magnitude better accuracy than existing IMEX Runge-Kutta methods, and/or achieve a given accuracy several times times faster in terms of computational runtime.
翻译:在本文中,我们将多元时间整合框架概括为累积性分解初始值问题。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,这些添加式集成器具有任意的准确性和不同程度的隐含性。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和隐含的多元块块方法,这些方法基于完全隐含的龙格-库塔方法,并具有高等级的顺序。我们表明,新的完全隐含性集成器(FIMEX)比现有的IMEX-龙格-库塔方法更加稳定,同时由于在解决非线性方程式相关系统的先决条件技术方面的最新发展,也提高了计算效率。对于以隐含部分无关紧要的定期域的PDE,我们将展示如何利用右侧评价与现有序列IMEX-龙格-库塔方法的平行化获得显著的加速率。对于(空间中的)固定离散化方法而言,新的方法可以实现比现有时间级的精确度的精确度。