📝 SummarXiv

Abstract

In this work, we quantify the timescales and information flow associated with multiscale energy transfer in a weakly turbulent system through a greedy optimization algorithm which looks to find the maximum conditional mutual information across lagged embeddings of time series localized by wavenumber. Our results provide a detailed understanding of how wavenumbers couple in order to maintain the weakly turbulent dynamic equilibrium. The algorithm is able to detect and characterize detailed differences of information flow with corresponding distinctions in time scales for both the forward and inverse cascades, and this can be done over both fast and slower time scales. This points to our approach being of broader applicability in real-world data coming from chaotic or turbulent dynamical systems.