For gradient flows, the existing structure-preserving schemes are difficult to achieve arbitrary high-order accuracy in time while preserving maximum-principle (MBP) and energy dissipating simultaneously. In this paper, we develop a new framework for constructing structure-preserving schemes which shall preserve those nice properties. By introducing KKT-conditions for energy dissipating and bound-preserving, we rewrite the original gradient flow into an expanded and coupled system. We shall utilize a novel predictor-corrector-corrector framework, termed the PCC method, which consists of a prediction from any numerical scheme to the user's favor, followed by two correction steps designed to enforce energy stability and MBP, respectively. We take the exponential time differencing Runge-Kutta scheme (ETDRK) as an example and establish the unique solvability and robust error analysis for our new framework. Extensive numerical experiments are provided to validate the efficiency and accuracy of our new approach. Enough numerical comparisons with the existing popular schemes are shown that our structure-preserving schemes can avoid numerical oscillations and capture the exact evolution of energy.
翻译:针对梯度流问题,现有保结构格式难以在时间上同时实现任意高阶精度、保持最大原理(MBP)与能量耗散特性。本文提出一种构建保结构格式的新框架,旨在同时保持这些优良性质。通过引入能量耗散与保界的KKT条件,我们将原始梯度流重写为一个扩展的耦合系统。我们将采用一种新颖的预测-校正-校正框架,称为PCC方法,该方法首先使用任意用户偏好的数值格式进行预测,随后通过两个校正步骤分别强制实现能量稳定性与MBP。以指数时间差分龙格-库塔格式(ETDRK)为例,我们为新框架建立了唯一可解性及鲁棒误差分析。大量数值实验验证了新方法的高效性与精确性。与现有主流格式的充分数值对比表明,我们的保结构格式能够避免数值振荡并精确捕捉能量演化过程。