Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm aimed at accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated in parallel using the expensive integrator. The set of sampled initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on low-dimensional systems of ordinary differential equations (ODEs). We provide numerical evidence that when the number of sampled initial values is large enough, stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm, maintaining solution accuracy. Given its stochastic nature, we also highlight that multiple simulations of stochastic parareal return a distribution of solutions that can represent a measure of uncertainty over the ODE solution.
翻译:超现实是数值整合基于时间的差别方程式的精密算法, 其方法是通过平行的时间域。 根据每个时间子间间隔的粗略初始值, 算法将解决方案定位在使用预测器- 校正器的固定迭代数中, 一旦满足了容忍度, 就会停止。 这个迭代进程将廉价( 粗分辨率) 和昂贵( 分辨率) 数字聚合器的解决方案结合起来。 在本文中, 我们引入了一种随机的模拟半现实算法, 目的是加速确定性模拟准方程式的趋同。 而不是提供预测者- 校正方程式的大约初步值, 以确定性准确性定值的一组, 由每个时间性次间间隔的概率差差的概率分布所得出的初步值。 然后, 所有样本都同时使用昂贵的内分解器。 抽样初始值的轨迹将最连续的( mogest) 轨迹反馈到连续的预测器- 校正调, 混固度比稳定性偏差值的比确定性数值的精确度分析器的精确度分析法, 其初始值的返回的精确值要少一些概率。, 我们的数值的模型的演化的演化过程的演化过程的演化过程, 的演化过程, 的演化的演化的演算法, 的演化的演化的演化的演化, 其演化的演化过程的演化过程, 的演化过程的演化过程, 的演化的演化的演化过程的演化的演化过程, 的演化, 的演化的演化, 的演化的演化的演化的演化的演化的演化, 的演化的演化的演化的演化的演化, 的演化的演化的演化, 的演化的演化的演化的演化, 的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的演化的