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arXiv · 2511.12583

Data-driven computation for periodic stochastic differential equations

Yao Li·Jiatong Sun
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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.

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Yao Li, Jiatong Sun. 2025-11-16. Data-driven computation for periodic stochastic differential equations. https://arxiv.org/abs/2511.12583

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