| Abstract: |
| In this work, we quantify the timescales and magnitude of information flow associated with multiscale energy transfer in the Majda-McLaughlin-Tabak model. As we show, both forward and inverse cascade features are detected. However, our method allows for a more intricate picture to emerge which quantifies how wave mixing allows for widely disparate scales to be causally linked. This should be of use in a range of similar problems, and thus we present a method that, beyond canonical averaging techniques, provides a more detailed characterization of chaotic multiscale flows. |
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