Linear-inverse-modeling approach to estimating Liang-Kleeman information flow in a cyclostationary process under
Justin Lien1, Hiroyasu Ando2, Yong-Yub Kim3
1Tohoku University, Mathematical Institute, Sendai, Japan.
Physical Review. E
|December 23, 2025
Summary
We introduce a new data-driven method combining Liang-Kleeman (LK) information flow and linear inverse modeling (LIM) to quantify causality and system dynamics. This framework reveals entropy transfer and offers insights into ocean variability.
Area of Science:
- Earth and Environmental Sciences
- Oceanography
- Climate Science
Background:
- The Liang-Kleeman (LK) information flow quantifies causality among variables.
- Linear inverse modeling (LIM) studies system dynamics from input data.
Purpose of the Study:
- To unify LK information flow and LIM into a data-driven framework (LIM-LK).
- To estimate information flow and quantify causality and system dynamics.
- To analyze entropy transfer from the environment to a system.
Main Methods:
- Developed the LIM-LK framework, a data-driven approach.
- Applied LIM to estimate LK information flow from observational data.
- Quantified causality among state variables and entropy transfer.
Main Results:
- The LIM-LK framework successfully estimates information flow and connects causality with system dynamics.
- Quantified entropy transfer from the environment via stochastic forcing.
- Demonstrated application to Pacific-Indian Ocean interactions.
Conclusions:
- The unified LIM-LK framework provides causal and dynamical insights into complex systems.
- Offers a novel method for analyzing ocean variability and seasonal modulation.
- Highlights the importance of integrating causality and dynamics in data-driven modeling.
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