随机微分方程大时间步长模拟的七联盟格式的GPU加速
GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations
摘要：
蒙特卡罗模拟被广泛用于数值求解随机微分方程。虽然该方法灵活且易于实现，但它可能收敛缓慢。此外，当使用大的时间步长时，将导致不准确的解。Seven League方案是一种基于深度学习的数值方法，已被提出来解决这些问题。本文推广了并行计算的方案，特别是在图形处理器（GPU）上，提高了计算速度。
英文摘要：
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
