| Autor: |
Farnik Nikakhtar, Laya Parkavousi, Muhammad Sahimi, M Reza Rahimi Tabar, Ulrike Feudel, Klaus Lehnertz |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
New Journal of Physics, Vol 25, Iss 8, p 083025 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1367-2630 |
| DOI: |
10.1088/1367-2630/acec63 |
| Popis: |
Stochastic processes are encountered in many contexts, ranging from generation sizes of bacterial colonies and service times in a queueing system to displacements of Brownian particles and frequency fluctuations in an electrical power grid. If such processes are Markov, then their probability distribution is governed by the Kramers–Moyal (KM) equation, a partial differential equation that involves an infinite number of coefficients, which depend on the state variable. The KM coefficients must be evaluated based on measured time series for a data-driven reconstruction of the governing equations for the stochastic dynamics. We present an accurate method of computing the KM coefficients, which relies on computing the coefficients’ conditional moments based on the statistical moments of the time series. The method’s advantages over state-of-the-art approaches are demonstrated by investigating prototypical one-dimensional stochastic processes with well-known properties. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
