Data-driven computation for periodic stochastic differential equations
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915636348715008 |
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| author | Li, Yao Sun, Jiatong |
| author_facet | Li, Yao Sun, Jiatong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven computation for periodic stochastic differential equations Li, Yao Sun, Jiatong Numerical Analysis 65C99, 68T07, 60H10 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. |
| title | Data-driven computation for periodic stochastic differential equations |
| topic | Numerical Analysis 65C99, 68T07, 60H10 |
| url | https://arxiv.org/abs/2511.12583 |