Data-driven computation for periodic stochastic differential equations
Numerical Analysis
2025-11-26 v2 Numerical Analysis
Abstract
Many stochastic differential equations in various applications like coupled neuronal oscillators are driven by time-periodic forces. In this paper, we extend several data-driven computational tools from autonomous Fokker-Planck equation to the time-periodic setting. This allows us to efficiently compute the time-periodic invariant probability measure using either grid-base method or artificial neural network solver, and estimate the speed of convergence towards the time-periodic invariant probability measure. We analyze the convergence of our algorithms and test their performances with several numerical examples.
Cite
@article{arxiv.2511.12583,
title = {Data-driven computation for periodic stochastic differential equations},
author = {Yao Li and Jiatong Sun},
journal= {arXiv preprint arXiv:2511.12583},
year = {2025}
}