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arXiv 2602.06491math.NAcs.NA
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显式自适应时间步长方法在具有局部Lipschitz系数的随机扩散系统中的强收敛性

An explicit adaptive time-stepping scheme for superlinear stochastic diffusion systems

Tiangong University(天津工业大学);Northeast Normal University(东北师范大学)

Xueqi Wen, Guozhen Li, Yuanping Cui, Xiaoyue Li

AI总结:

本文提出了一种自适应时间步长方法,用于求解具有局部Lipschitz系数的随机扩散系统,实现了强收敛性,并通过实验验证了其计算效率的提升。

摘要:

本文提出了一种自适应时间步长方法,用于求解漂移和扩散系数为局部Lipschitz连续且可能具有多项式增长的随机扩散系统。通过控制漂移和扩散系数的增长,我们给出了状态依赖的自适应时间步长选择,并建立了所提出方案的强收敛性,收敛阶为最优的1/2。将自适应时间步长方案的性能与几种广泛使用的显式和隐式方案,包括tamed EM、截断EM和backward EM方案进行了比较。对刚性、非刚性和高维随机扩散系统进行的数值实验验证了所提出方案的计算效率提升,并验证了理论结果。

英文摘要:

This paper develops an adaptive time-stepping Euler--Maruyama (EM) scheme for stochastic diffusion systems with superlinearly growing coefficients. The adaptive timestep is chosen according to the superlinear growth of both drift and diffusion coefficients. To prevent excessively small timesteps, a truncated EM scheme is employed as a backstop whenever the adaptive timestep falls below a prescribed threshold. By combining the stochastic analysis with the stopping time technique, we establish the strong convergence of the proposed method and obtain the optimal $1/2$-order strong convergence rate in the $L^q$-sense for $q>2$. {Finally, numerical experiments are carried out for stiff, nonstiff, and stochastic Lorenz systems to validate the theoretical findings. The results indicate that the proposed scheme achieves superior accuracy and performance compared to various fixed-step and adaptive alternatives.

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。

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