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Information-theoretic approaches to prediction of transitions in complex systems

Standard - GACR GA25-18105S [Registered results] 2025 - 2027

Principal Investigator: RNDr. Milan Paluš, DrSc.

This project aims at further development, original extensions and novel applications of state-ofthe-art methods that lie on the borderline between information theory, nonlinear dynamics and statistical physics to study and identify precursors and warning signals of transitions (critical or benign) in complex systems.

Considering multiscale and non-Gaussian character of complex systems, state-of-the-art methods and techniques from information theory, nonlinear dynamical systems and (non-equilibrium) statistical physics will be used to develop measures of complexity and causal information flows and information geometry in different entropy frameworks (Shannon, Rényi and Tsallis).

Primary application fields will be in neuroscience (epilepsy), geosciences (climate and space weather) and small-size complex systems.