Information-theoretic formulation of dynamical systems: Causality, modeling, and control

The problems of causality, modeling, and control for chaotic, high-dimensional dynamical systems are formulated in the language of information theory. The central quantity of interest is the Shannon entropy, which measures the amount of information in the states of the system. Within this framework, causality is quantified by the information flux among the variables of interest in the dynamical system. Reduced-order modeling is posed as a problem related to the conservation of information in which models aim at preserving the maximum amount of relevant information from the original system Similarly, control theory is cast in information-theoretic terms by envisioning the tandem sensor-actuator as a device reducing the unknown information of the state to be controlled. The new formulation is used to address three problems about the causality, modeling, and control of turbulence, which stands as a primary example of a chaotic, high-dimensional dynamical system. The applications include the causality of the energy transfer in the turbulent cascade, subgrid-scale modeling for large-eddy simulation, and flow control for drag reduction in wall-bounded turbulence.

Automated summary

This study formulates the problems of causality, modeling, and control for chaotic, high-dimensional dynamical systems in the language of information theory, with the Shannon entropy as the central quantity of interest. Causality is quantified by the information flux among the variables, reduced-order modeling aims at preserving relevant information, and control theory envisions the sensor-actuator as reducing unknown information of the state. The study applies this framework to address problems in turbulence, including energy transfer causality, subgrid-scale modeling, and flow control for drag reduction.