Identifying dynamical structures in the physical space of stochastic processes
© CopyrightEPLA, 2019.Characterizing dynamical patterns in the (physical) state space of stochastic processes can be a challenging task. From two visualization techniques, the observable-representation and k-means clustering, a unified framework to identify such structures is developed. The only inf...
| Autores: | ; ; ; ; ; |
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| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | UDESC |
| Repositorio: | Repositório Institucional da Udesc |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:repositorio.udesc.br:UDESC/5916 |
| Acceso en línea: | https://repositorio.udesc.br/handle/UDESC/5916 |
| Nivel de acceso: | Acceso abierto |
| Sumario: | © CopyrightEPLA, 2019.Characterizing dynamical patterns in the (physical) state space of stochastic processes can be a challenging task. From two visualization techniques, the observable-representation and k-means clustering, a unified framework to identify such structures is developed. The only information required is the system transition matrix R (a quantity that can be directly accessed from experimental data). The approach is illustrated through the analysis of random searches for targets distributed in patchy environments. The protocol - for R constructed from a typical tracked long trajectory - is able to reveal the shape and locations of all the landscape patches. The method constitutes a valuable new tool to study the underlying geometry of general stochastic processes. |
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