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Enfoque de modelado lineal-inverso para estimar el flujo de información de Liang-Kleeman en un proceso
Justin Lien1, Hiroyasu Ando2, Yong-Yub Kim3
1Tohoku University, Mathematical Institute, Sendai, Japan.
Physical review. E
|December 23, 2025
Resumen
Introducimos un nuevo método impulsado por datos que combina el flujo de información de Liang-Kleeman (LK) y el modelado lineal inverso (LIM) para cuantificar la causalidad y la dinámica del sistema. Este marco revela la transferencia de entropía y ofrece información sobre la variabilidad oceánica.
Área de la Ciencia:
- Ciencias de la Tierra y del Medio Ambiente
- Oceanografía
- Ciencia del Clima
Sus antecedentes:
- El flujo de información de Liang-Kleeman (LK) cuantifica la causalidad entre variables.
- El modelado lineal inverso (LIM) estudia la dinámica del sistema a partir de datos de entrada.
Objetivo del estudio:
- Unificar el flujo de información LK y LIM en un marco impulsado por datos (LIM-LK).
- Estimar el flujo de información y cuantificar la causalidad y la dinámica del sistema.
- Analizar la transferencia de entropía del entorno a un sistema.
Principales métodos:
- Se desarrolló el marco LIM-LK, un enfoque impulsado por datos.
- Se aplicó LIM para estimar el flujo de información LK a partir de datos observacionales.
- Se cuantificó la causalidad entre las variables de estado y la transferencia de entropía.
Principales resultados:
- El marco LIM-LK estima con éxito el flujo de información y conecta la causalidad con la dinámica del sistema.
- Se cuantificó la transferencia de entropía del entorno a través de la forzación estocástica.
- Se demostró la aplicación a las interacciones entre el Océano Pacífico y el Índico.
Conclusiones:
- El marco unificado LIM-LK proporciona información causal y dinámica sobre sistemas complejos.
- Ofrece un método novedoso para analizar la variabilidad oceánica y la modulación estacional.
- Destaca la importancia de integrar la causalidad y la dinámica en el modelado impulsado por datos.
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